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381.476L12.686 187.132c-9.373-9.373-9.373-24.569 0-33.941l22.667-22.667c9.357-9.357 24.522-9.375 33.901-.04L224 284.505l154.745-154.021c9.379-9.335 24.544-9.317 33.901.04l22.667 22.667c9.373 9.373 9.373 24.569 0 33.941L240.971 381.476c-9.373 9.372-24.569 9.372-33.942 0z"></path></svg></div></div></div><div pointer-events="none" class="sc-6293d692-0 sc-610ba7e0-0 fqJJID gkvAJG"><div class="sc-ace17a57-0 iIvmda"><div class="sc-ace17a57-0 fXUJvn"><div class="sc-ace17a57-0 jbARwL"><div class="sc-ace17a57-0 eFYjSa"><div class="sc-ace17a57-0 gYFcFc"><span display="block" width="auto" class="sc-b323b31-0 gIJLbd">APIs</span></div><div class="sc-ace17a57-0 eTCxuy"><div class="sc-ace17a57-0 ldsYPN"><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC bGaVbc"><a rel="" target="" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe" href="/product/voice-agent-api"><div class="sc-ace17a57-0 gCajMv"><div class="sc-ace17a57-0 sc-3618c394-4 ftcNci bpvjgx"><div 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decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060077-speech-to-text.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dVEgqo">Speech to Text API</span><span font-size="textXs" class="sc-b323b31-0 sc-3618c394-14 lnJhVG cFsKfJ">Unmatched accuracy, speed & cost</span></div></div></a></div><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC bGaVbc"><a rel="" target="" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe" href="/product/audio-intelligence"><div class="sc-ace17a57-0 gCajMv"><div class="sc-ace17a57-0 sc-3618c394-4 ftcNci bpvjgx"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" 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MuiButton-colorSecondary MuiButton-root MuiButton-text MuiButton-textSecondary MuiButton-sizeSmall MuiButton-textSizeSmall MuiButton-colorSecondary sc-d74559df-0 fqnlbI sc-6e03a9e4-0 iumJHL sc-3618c394-0 gxydGy css-6n9fb7" tabindex="0" type="button" __typename="ComponentCallToActionRecord" id="Xho9X9_YQj2HBRVr87BG0g" href="/learn?type=Article&page=1&category=Announcements"><span class="sc-b323b31-0 fcPowg button-label" display="inline-flex"><span>View more</span></span><style data-emotion="css kcxyz4">.css-kcxyz4{display:inherit;margin-right:-2px;margin-left:8px;}.css-kcxyz4>*:nth-of-type(1){font-size:18px;}</style><span class="MuiButton-icon MuiButton-endIcon MuiButton-iconSizeSmall css-kcxyz4"><div width="fit-content" class="sc-ace17a57-0 ddioGD"><span display="inline-flex" class="sc-b323b31-0 bZmEHx"><svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 448 512" height="16" width="16" xmlns="http://www.w3.org/2000/svg"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"></path></svg></span></div></span></a></div><div class="sc-ace17a57-0 gzWdaa"><a rel="" target="" display="-webkit-box" height="100%" width="100%" class="sc-e69ac761-0 ecplwP sc-2850a57f-3 cPKdIq" href="/learn/introducing-nova-3-speech-to-text-api"><div width="100%" height="100%" id="e1wy33mNTlGk8-WqDH28_Q" class="sc-ace17a57-0 sc-2850a57f-1 fJWxEy ggobuO"><div width="100%" height="150px" class="sc-6293d692-0 eWcIvK"><div class="sc-ace17a57-0 sc-5f7d928-0 fvbMGK image-wrapper" width="100%" height="100%"><img alt="Introducing Nova-3: Setting a New Standard for AI-Driven Speech-to-Text" loading="lazy" width="590" height="259" decoding="async" data-nimg="1" class="sc-2850a57f-2 dSsmQU" style="color:transparent;object-fit:cover" srcSet="/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1740086862-2502-nova-3-blog-header.jpg%3Fauto%3Dformat%26w%3D590&w=640&q=75 1x, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1740086862-2502-nova-3-blog-header.jpg%3Fauto%3Dformat%26w%3D590&w=1200&q=75 2x" src="/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1740086862-2502-nova-3-blog-header.jpg%3Fauto%3Dformat%26w%3D590&w=1200&q=75"/></div></div><div class="sc-ace17a57-0 biWqHE"><span font-weight="semiBold" class="sc-b323b31-0 iNRMrC"><svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 320 512" color="#ffffff" class="sc-2850a57f-0 Tcnof" style="color:#ffffff" height="12" width="12" xmlns="http://www.w3.org/2000/svg"><path d="M285.476 272.971L91.132 467.314c-9.373 9.373-24.569 9.373-33.941 0l-22.667-22.667c-9.357-9.357-9.375-24.522-.04-33.901L188.505 256 34.484 101.255c-9.335-9.379-9.317-24.544.04-33.901l22.667-22.667c9.373-9.373 24.569-9.373 33.941 0L285.475 239.03c9.373 9.372 9.373 24.568.001 33.941z"></path></svg>Introducing Nova-3: Setting a New Standard for AI-Driven Speech-to-Text</span><span class="sc-b323b31-0 jUNaqu">Published on 02/12/25</span></div></div></a></div></div></div></div></li><li height="fit-content" width="100%" tabindex="0" class="sc-6293d692-0 hEIHLV"><div width="100%" class="sc-ace17a57-0 gbwWMz"><div height="fit-content" width="100%" class="sc-ace17a57-0 caCSrG"><span font-size="textXl" font-weight="medium" display="block" cursor="pointer" width="100%" class="sc-b323b31-0 kzoxAi">Solutions</span><div transform="rotateX(0deg)" class="sc-ace17a57-0 fxtsZN"><svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 448 512" color="#A9A9AD" style="color:#A9A9AD" height="12" width="12" xmlns="http://www.w3.org/2000/svg"><path d="M207.029 381.476L12.686 187.132c-9.373-9.373-9.373-24.569 0-33.941l22.667-22.667c9.357-9.357 24.522-9.375 33.901-.04L224 284.505l154.745-154.021c9.379-9.335 24.544-9.317 33.901.04l22.667 22.667c9.373 9.373 9.373 24.569 0 33.941L240.971 381.476c-9.373 9.372-24.569 9.372-33.942 0z"></path></svg></div></div></div><div pointer-events="none" class="sc-6293d692-0 sc-610ba7e0-0 fqJJID gkvAJG"><div class="sc-ace17a57-0 iIvmda"><div class="sc-ace17a57-0 fXUJvn"><div class="sc-ace17a57-0 jbARwL"><div class="sc-ace17a57-0 exshrD"><div class="sc-ace17a57-0 gYFcFc"><span display="block" width="auto" class="sc-b323b31-0 gIJLbd">Use Cases</span></div><div class="sc-ace17a57-0 eTCxuy"><div class="sc-ace17a57-0 ldsYPN"><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC kjWGMU"><a rel="" target="" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe" href="/solutions/contact-centers"><div class="sc-ace17a57-0 ljypZA"><div class="sc-ace17a57-0 sc-3618c394-4 iNSltq iIhisP"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" 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guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dRVqCB">Medical Transcription</span></div></div></a></div><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC kjWGMU"><a rel="" target="" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe" href="/deepgram-for-voicebots-and-chatbots"><div class="sc-ace17a57-0 ljypZA"><div class="sc-ace17a57-0 sc-3618c394-4 iNSltq iIhisP"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060265-conversational-ai.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dRVqCB">Conversational AI</span></div></div></a></div><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC kjWGMU"><a rel="" target="" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe" href="/solutions/speech-analytics"><div class="sc-ace17a57-0 ljypZA"><div class="sc-ace17a57-0 sc-3618c394-4 iNSltq iIhisP"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060282-speech-analytics.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dRVqCB">Speech Analytics</span></div></div></a></div><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC kjWGMU"><a rel="" target="" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe" href="/solutions/media-transcription"><div class="sc-ace17a57-0 ljypZA"><div class="sc-ace17a57-0 sc-3618c394-4 iNSltq iIhisP"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060298-media.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dRVqCB">Media Transcription</span></div></div></a></div></div></div></div><div class="sc-ace17a57-0 bcBFEg"><a rel="" target="" class="sc-e69ac761-0 bkHGsI sc-3618c394-11 cTfqJK" href="/customers"><div class="sc-ace17a57-0 ldePaW"><div width="28px" height="28px" class="sc-6293d692-0 sc-3618c394-1 ipShUF kXFUqt"><div class="sc-ace17a57-0 sc-5f7d928-0 fvbMGK image-wrapper" width="100%" height="100%"><img alt="Customers" loading="lazy" width="16" height="16" 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fxtsZN"><svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 448 512" color="#A9A9AD" style="color:#A9A9AD" height="12" width="12" xmlns="http://www.w3.org/2000/svg"><path d="M207.029 381.476L12.686 187.132c-9.373-9.373-9.373-24.569 0-33.941l22.667-22.667c9.357-9.357 24.522-9.375 33.901-.04L224 284.505l154.745-154.021c9.379-9.335 24.544-9.317 33.901.04l22.667 22.667c9.373 9.373 9.373 24.569 0 33.941L240.971 381.476c-9.373 9.372-24.569 9.372-33.942 0z"></path></svg></div></div></div><div pointer-events="none" class="sc-6293d692-0 sc-610ba7e0-0 fqJJID gkvAJG"><div class="sc-ace17a57-0 iIvmda"><div class="sc-ace17a57-0 fXUJvn"><div class="sc-ace17a57-0 jbARwL"><div class="sc-ace17a57-0 exshrD"><div class="sc-ace17a57-0 gYFcFc"><span display="block" width="auto" class="sc-b323b31-0 gIJLbd">Resources</span></div><div class="sc-ace17a57-0 eTCxuy"><div class="sc-ace17a57-0 ldsYPN"><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC kjWGMU"><a rel="" 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class="sc-b323b31-0 sc-3618c394-13 hHDluE dRVqCB">AI Glossary</span></div></div></a></div><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC kjWGMU"><a rel="" target="" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe" href="/about"><div class="sc-ace17a57-0 ljypZA"><div class="sc-ace17a57-0 sc-3618c394-4 iNSltq iIhisP"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060599-speech-bubble.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dRVqCB">About</span></div></div></a></div><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC kjWGMU"><a rel="" target="" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe" href="/careers"><div class="sc-ace17a57-0 ljypZA"><div class="sc-ace17a57-0 sc-3618c394-4 iNSltq iIhisP"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060617-careers.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dRVqCB">Careers</span></div></div></a></div></div></div></div><div class="sc-ace17a57-0 bcBFEg"><a rel="" target="" class="sc-e69ac761-0 bkHGsI sc-3618c394-11 cTfqJK" href="/ai-apps"><div class="sc-ace17a57-0 ldePaW"><div width="28px" height="28px" class="sc-6293d692-0 sc-3618c394-1 ipShUF kXFUqt"><div class="sc-ace17a57-0 sc-5f7d928-0 fvbMGK image-wrapper" width="100%" height="100%"><img alt="AI Apps" 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alt="Transcription Tool" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" class="sc-3618c394-8 jzJMzZ" style="color:transparent;object-fit:cover" src="https://www.datocms-assets.com/96965/1728060712-transcription.svg"/></div></div><span font-size="textSm" class="sc-b323b31-0 sc-3618c394-15 fmcyDO gpxdfo">Transcription Tool</span></div></a></div></div></div></div></div></li><li height="fit-content" width="100%" tabindex="0" class="sc-6293d692-0 hEIHLV"><div width="100%" class="sc-ace17a57-0 gbwWMz"><div height="fit-content" width="100%" class="sc-ace17a57-0 caCSrG"><span font-size="textXl" font-weight="medium" display="block" cursor="pointer" width="100%" class="sc-b323b31-0 kzoxAi">Developers</span><div transform="rotateX(0deg)" class="sc-ace17a57-0 fxtsZN"><svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 448 512" color="#A9A9AD" style="color:#A9A9AD" height="12" width="12" xmlns="http://www.w3.org/2000/svg"><path d="M207.029 381.476L12.686 187.132c-9.373-9.373-9.373-24.569 0-33.941l22.667-22.667c9.357-9.357 24.522-9.375 33.901-.04L224 284.505l154.745-154.021c9.379-9.335 24.544-9.317 33.901.04l22.667 22.667c9.373 9.373 9.373 24.569 0 33.941L240.971 381.476c-9.373 9.372-24.569 9.372-33.942 0z"></path></svg></div></div></div><div pointer-events="none" class="sc-6293d692-0 sc-610ba7e0-0 fqJJID gkvAJG"><div class="sc-ace17a57-0 iIvmda"><div class="sc-ace17a57-0 fXUJvn"><div class="sc-ace17a57-0 jbARwL"><div class="sc-ace17a57-0 exshrD"><div class="sc-ace17a57-0 eTCxuy"><div class="sc-ace17a57-0 ldsYPN"><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC kjWGMU"><a href="https://developers.deepgram.com/documentation/" rel="noreferrer noopener" target="_blank" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe"><div class="sc-ace17a57-0 ljypZA"><div class="sc-ace17a57-0 sc-3618c394-4 iNSltq iIhisP"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060846-documentation.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dRVqCB">Documentation</span></div></div></a></div><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC kjWGMU"><a rel="" target="" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe" href="/learn?type=Article&page=1&category=Tutorials"><div class="sc-ace17a57-0 ljypZA"><div class="sc-ace17a57-0 sc-3618c394-4 iNSltq iIhisP"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060860-tutorials.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dRVqCB">Tutorials</span></div></div></a></div><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC kjWGMU"><a href="https://status.deepgram.com/" rel="noreferrer noopener" target="_blank" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe"><div class="sc-ace17a57-0 ljypZA"><div class="sc-ace17a57-0 sc-3618c394-4 iNSltq iIhisP"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060872-status.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 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sc-3618c394-5 dMJoKC kjWGMU"><a href="https://developers.deepgram.com/on-prem/" rel="noreferrer noopener" target="_blank" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe"><div class="sc-ace17a57-0 ljypZA"><div class="sc-ace17a57-0 sc-3618c394-4 iNSltq iIhisP"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060943-on-prem.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dRVqCB">Self-Hosted Deployment</span></div></div></a></div><div width="max-content" class="sc-ace17a57-0 sc-3618c394-5 dMJoKC kjWGMU"><a href="https://github.com/orgs/deepgram/discussions/categories/general-help" rel="noreferrer noopener" target="_blank" text-decoration="none" class="sc-e69ac761-0 cCnzAI sc-3618c394-10 jycipe"><div class="sc-ace17a57-0 ljypZA"><div class="sc-ace17a57-0 sc-3618c394-4 iNSltq iIhisP"><div class="sc-ace17a57-0 sc-5f7d928-0 iMdtXa kritbo image-wrapper" width="fit-content" height="fit-content"><img alt="" loading="lazy" width="16" height="16" decoding="async" data-nimg="1" style="color:transparent;object-fit:contain" src="https://www.datocms-assets.com/96965/1728060960-question.svg"/></div></div><div class="sc-ace17a57-0 guuHrd"><span font-weight="medium" display="flex" tabindex="-1" class="sc-b323b31-0 sc-3618c394-13 hHDluE dRVqCB">Help</span></div></div></a></div></div></div></div><div class="sc-ace17a57-0 bcBFEg"><a href="https://playground.deepgram.com/" rel="noreferrer noopener" target="_blank" class="sc-e69ac761-0 bkHGsI sc-3618c394-11 dAEPVD"><div class="sc-ace17a57-0 ldePaW"><div width="28px" height="28px" class="sc-6293d692-0 sc-3618c394-1 ipShUF kXFUqt"><div class="sc-ace17a57-0 sc-5f7d928-0 fvbMGK 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16px;border-radius:4px;-webkit-transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;box-shadow:0px 3px 1px -2px rgba(0,0,0,0.2),0px 2px 2px 0px rgba(0,0,0,0.14),0px 1px 5px 0px rgba(0,0,0,0.12);min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;background:#0B0B0C;color:#ffffff;border:1px solid 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data-emotion="css ubnyan">.css-ubnyan{display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;position:relative;box-sizing:border-box;-webkit-tap-highlight-color:transparent;background-color:transparent;outline:0;border:0;margin:0;border-radius:0;padding:0;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;vertical-align:middle;-moz-appearance:none;-webkit-appearance:none;-webkit-text-decoration:none;text-decoration:none;color:inherit;font-family:var(--font-inter, sans-serif);font-size:0.875rem;line-height:1.75rem;font-weight:700;text-transform:uppercase;min-width:64px;padding:6px 16px;border-radius:4px;-webkit-transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms 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tabindex="0" type="button" href="https://console.deepgram.com/signup" target="_self" id="108492199" __typename="ComponentCallToActionRecord"><span class="sc-b323b31-0 btXZGI button-label" display="inline-flex"><span>Sign Up</span></span></a></div></ul></div></div></div><div display="none" id="nav-overlay" height="0" class="sc-ace17a57-0 jUgQHG"></div></div></nav></div></header><section id="" width="100%" class="sc-ace17a57-0 sc-b22001da-2 iLVWKa eAcnIZ"><div width="100%" class="sc-6293d692-0 chAbBM"><div class="sc-ace17a57-0 doHfou"><a rel="" target="" href="/ai-glossary" class="sc-e69ac761-0 fobbYG"><h2 font-size="displayLg" font-family="primaryFont" class="sc-4555ca6a-0 htUWGk">AI Glossary</h2></a></div></div></section><section id="" width="100%" class="sc-ace17a57-0 sc-b22001da-2 hMGZHr dJJSTR"><div width="100%" class="sc-6293d692-0 chAbBM"><div height="100%" width="100%" class="sc-6293d692-0 gKLLRj"><div height="100%" width="100%" class="sc-662dedcb-0 qCkfD"><div class="sc-6293d692-0 csGpzP"><div class="sc-ace17a57-0 hhtFqm glossary-content-wrapper"><div class="sc-ace17a57-0 btkfVN"><div class="sc-ace17a57-0 sc-a48d0877-0 dJCIVv zIKHU"><h1 font-size="displaySm" letter-spacing="neg2" font-weight="semiBold" font-family="primaryFont" class="sc-b323b31-0 JYZsj">Generative Adversarial Networks (GANs)</h1><div class="sc-ace17a57-0 fJkbQy"><p font-family="bodyFont" class="sc-5159831f-0 GBftN">Generative Adversarial Networks (GANs) are generative models developed by combining two neural network architectures: the generator and the discriminator. In this setup, both networks compete as “adversaries.” The generator's primary role is to create new data samples, and the discriminator's job is to assess their authenticity by distinguishing them from actual samples.</p></div></div></div><div class="sc-ace17a57-0 sc-4e888a2-0 hfDdWp cifBqP"><h2 id="introduction-to-generative-adversarial-neural-networks-gans" class="sc-4555ca6a-0 sc-42818e5a-3 kIEulY ipqlKg">Introduction to Generative Adversarial Neural Networks (GANs)</h2><p class="sc-5159831f-0">Generative Adversarial Networks (GANs) are generative models developed by combining two neural network architectures: the generator and the discriminator. In this setup, both networks compete as “adversaries.” The generator's primary role is to create new data samples, and the discriminator's job is to assess their authenticity by distinguishing them from actual samples.</p><p class="sc-5159831f-0">GANs typically use unsupervised learning techniques to learn from the data distribution without needing predefined labels. Through the adversarial relationship (competition) between the generator and discriminator, the model progressively improves itself to produce outputs that closely align with the expected distribution, creating new, realistic samples that are hard to tell from real samples.</p><p class="sc-5159831f-0">Their ability to generate high-quality, realistic data has made GANs popular across various domains. They are widely used in artistic applications like style transfer and synthetic data creation.</p><p class="sc-5159831f-0"><div width="100%" class="sc-ace17a57-0 ldWHw component-img-wrapper" id="Z9Xp_t5MQrqzWfW1TE7PWA"><div class="sc-ace17a57-0 sc-5f7d928-0 iLvNR kritbo image-wrapper" width="fit-content" height="fit-content" display="flex"><img alt="" title="" loading="lazy" width="1920" height="1238" decoding="async" data-nimg="1" class="sc-3ec8aa52-0 jTMTOn desktop-image" style="color:transparent;object-fit:contain;background-size:contain;background-position:50% 50%;background-repeat:no-repeat;background-image:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 1920 1238'%3E%3Cfilter id='b' color-interpolation-filters='sRGB'%3E%3CfeGaussianBlur stdDeviation='20'/%3E%3CfeColorMatrix values='1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 100 -1' result='s'/%3E%3CfeFlood x='0' y='0' width='100%25' height='100%25'/%3E%3CfeComposite operator='out' in='s'/%3E%3CfeComposite in2='SourceGraphic'/%3E%3CfeGaussianBlur stdDeviation='20'/%3E%3C/filter%3E%3Cimage width='100%25' height='100%25' x='0' y='0' preserveAspectRatio='none' style='filter: url(%23b);' href='/deepgram.jpg'/%3E%3C/svg%3E")" sizes="(min-width: 992px) 810px, 100vw" srcSet="/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1701096542-image2.png&w=640&q=75 640w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1701096542-image2.png&w=750&q=75 750w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1701096542-image2.png&w=828&q=75 828w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1701096542-image2.png&w=1080&q=75 1080w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1701096542-image2.png&w=1200&q=75 1200w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1701096542-image2.png&w=1920&q=75 1920w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1701096542-image2.png&w=2048&q=75 2048w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1701096542-image2.png&w=3840&q=75 3840w" src="/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1701096542-image2.png&w=3840&q=75"/></div></div></p><h2 id="history-of-gans" class="sc-4555ca6a-0 sc-42818e5a-3 kIEulY ipqlKg">History of GANs</h2><p class="sc-5159831f-0">The journey of GANs began as a solution to the need for high-dimensional, realistic, and diverse data samples. Traditional machine learning approaches often struggled with handling complex and varied real-world data, prompting GANs to emerge as a solution that expanded the possibilities of generative models. This overview offers a concise history, highlighting various endeavors by key contributors in the evolution of GANs.</p><h3 id="2014-origin-of-gans" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">2014: Origin of GANs</h3><p class="sc-5159831f-0">Goodfellow et al. (2014) <a href="https://arxiv.org/abs/1406.2661"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">proposed the GAN framework</span></a>, introducing the generator (G) and discriminator (D) models to estimate generative models without relying on <a href="https://en.wikipedia.org/wiki/Markov_chain"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Markov chains</span></a> or unrolled <a href="https://en.wikipedia.org/wiki/Approximate_inference"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">approximate inference</span></a> networks, which were common in generative models of the time.</p><p class="sc-5159831f-0">GANs gained attention for their ability to generate realistic images, sparking increased research interest in this area.</p><h3 id="2015-introduction-of-dcgan" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">2015: Introduction of DCGAN</h3><p class="sc-5159831f-0">Radford et al. (2015) introduced <a href="https://arxiv.org/abs/1511.06434"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Deep Convolutional Generative Adversarial Networks (DCGAN)</span></a>, which set new <a href="https://deepgram.com/ai-glossary/benchmarking">benchmarks</a> for GAN architecture, focusing on generating realistic images through deep convolutional networks. This innovation improved stability and performance in image generation tasks. It introduced innovative concepts like <a href="https://machinelearningmastery.com/how-to-interpolate-and-perform-vector-arithmetic-with-faces-using-a-generative-adversarial-network/"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">image arithmetic</span></a>, which enabled extracting features from specific latent spaces by conditioning images on others.</p><p class="sc-5159831f-0">However, DCGAN faced limitations in the image resolution of the generated output, which led to the development of <a href="https://arxiv.org/abs/1809.11096"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">BigGANs</span></a> by Brock et al. (2018). This solution generates high-resolution and realistic images. BigGAN is recognized as one of the largest and most computationally intensive GAN models to date.</p><h3 id="2017-progressive-gan" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">2017: Progressive GAN</h3><p class="sc-5159831f-0">From NVIDIA, Karras et al. (2017) developed <a href="https://arxiv.org/abs/1710.10196"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Progressive GAN</span></a>, which scaled up images progressively, starting from smaller resolutions (like 4 x 4 or 16 x 16) and gradually increasing to larger sizes. This architecture improved the stability and detail of the image generated.</p><h3 id="2018-style-gan-1" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">2018: Style GAN 1</h3><p class="sc-5159831f-0">In <a href="https://arxiv.org/abs/1812.04948"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">the Style GAN 1 model</span></a>, each generator is conceptualized as a distinct style, with each style influencing effects at specific scales, such as coarse (overall structure or layout), middle (facial expressions or patterns), and delicate (lightning and shading or shape of nose) styles. </p><p class="sc-5159831f-0">The researchers achieved that by mapping images from the latent space, ‘z’, to the intermediate latent space, ‘w’, through a dedicated mapping network.</p><h3 id="2019-style-gan-2" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">2019: Style GAN 2</h3><p class="sc-5159831f-0"><a href="https://arxiv.org/abs/1912.04958"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">StyleGAN 2</span></a> improved upon its predecessor by addressing artifacts like phase and water droplet-like artifacts to enhance image quality and realism. It became a state-of-the-art model in GANs compared to its predecessors.</p><p class="sc-5159831f-0">There has been a great deal of improvement in the GAN model since 2014. Despite its transformative impact on generative modeling and image synthesis, ethical concerns have emerged due to issues like deepfake technology and intellectual property infringement, such as the notable case of <a href="https://www.itsnicethat.com/features/ones-to-watch-2020-robbie-barrat-digital-240220"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Robbie Barrat using GANs for AI art</span></a>. </p><p class="sc-5159831f-0">These concerns continue, but the GAN model continues to play a crucial role within the broader field of artificial intelligence and remains an important generative technology.</p><h2 id="the-architecture-of-gans" class="sc-4555ca6a-0 sc-42818e5a-3 kIEulY ipqlKg">The Architecture of GANs</h2><p class="sc-5159831f-0">At the heart of the GAN framework is adversarial learning, which involves the generator and the discriminator networks that work with and against each other as part of the architecture. </p><p class="sc-5159831f-0">This setup is a <a href="https://en.wikipedia.org/wiki/Zero-sum_game"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">zero-sum game</span></a> in <a href="https://en.wikipedia.org/wiki/Game_theory"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">game theory</span></a>, where the success of one participant comes at the expense of the other. The networks work together by fitting the training data and learning from the data by adjusting the weights based on the errors to minimize the loss. Each network trains to work against the other. The goal is to reach a state of equilibrium where the discriminator cannot reliably distinguish between real and fake samples.</p><p class="sc-5159831f-0">The generator initiates the process in GANs by producing artificial data from a random noise source. This data undergoes repeated improvements as the generator enhances its ability to mimic natural data distribution, resulting in outputs that progressively look more like actual data. Simultaneously, the discriminator assesses how genuine the generator's output appears, progressively learning to differentiate between real and generated data. </p><p class="sc-5159831f-0">As the discriminator improves at identifying generated data, it pushes the generator to create increasingly realistic data. As the training process continues, at some point, the discriminator finds it challenging to distinguish between generated and real data.</p><p class="sc-5159831f-0"><div width="100%" class="sc-ace17a57-0 ldWHw component-img-wrapper" id="UpBk_vE8TrGAMIQS1njkFg"><div class="sc-ace17a57-0 sc-5f7d928-0 iLvNR kritbo image-wrapper" width="fit-content" height="fit-content" display="flex"><img alt="" title="" loading="lazy" width="1920" height="1088" decoding="async" data-nimg="1" class="sc-3ec8aa52-0 jTMTOn desktop-image" style="color:transparent;object-fit:contain;background-size:contain;background-position:50% 50%;background-repeat:no-repeat;background-image:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 1920 1088'%3E%3Cfilter id='b' color-interpolation-filters='sRGB'%3E%3CfeGaussianBlur stdDeviation='20'/%3E%3CfeColorMatrix values='1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 100 -1' result='s'/%3E%3CfeFlood x='0' y='0' width='100%25' height='100%25'/%3E%3CfeComposite operator='out' in='s'/%3E%3CfeComposite in2='SourceGraphic'/%3E%3CfeGaussianBlur stdDeviation='20'/%3E%3C/filter%3E%3Cimage width='100%25' height='100%25' x='0' y='0' preserveAspectRatio='none' style='filter: url(%23b);' href='/deepgram.jpg'/%3E%3C/svg%3E")" sizes="(min-width: 992px) 810px, 100vw" srcSet="/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1701096525-image1.png&w=640&q=75 640w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1701096525-image1.png&w=750&q=75 750w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1701096525-image1.png&w=828&q=75 828w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1701096525-image1.png&w=1080&q=75 1080w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1701096525-image1.png&w=1200&q=75 1200w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1701096525-image1.png&w=1920&q=75 1920w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1701096525-image1.png&w=2048&q=75 2048w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1701096525-image1.png&w=3840&q=75 3840w" src="/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1701096525-image1.png&w=3840&q=75"/></div></div></p><h4 id="the-role-of-the-generator" class="sc-4555ca6a-0 sc-42818e5a-3 gSWJsZ ipqlKg">The role of the generator</h4><p class="sc-5159831f-0">The generator initializes by generating synthetic data from random noise. This initial data is often far from the desired output but serves as a starting point for training the network. The generator then observes the real data distribution and adjusts its outputs accordingly. This objective is to generate data that closely resembles the real data so that the discriminator cannot tell the difference. The training process (adjusting the weights to minimize the error) is progressive, with the generator learning from each interaction with the discriminator.</p><h4 id="the-role-of-the-discriminator" class="sc-4555ca6a-0 sc-42818e5a-3 gSWJsZ ipqlKg">The role of the discriminator</h4><p class="sc-5159831f-0">The role of the discriminator in the GAN architecture is to evaluate the authenticity of the data produced by the generator. It does this by distinguishing between actual data and the synthetic data created by the generator.</p><p class="sc-5159831f-0">The discriminator fits the training data and learns to discern the differences between real and synthetic data from the generator. The objective of the discriminator is to assign high probabilities to real data points and low probabilities to synthetic points. Over time, the discriminator's ability to distinguish between real and synthetic data becomes more refined, providing a moving target for the generator to improve against.</p><p class="sc-5159831f-0">In this context, it estimates the probability <span font-family="secondaryFont" class="sc-b323b31-0 sc-42818e5a-4 hqcSqI jpHeYz">P(Y | X = x) </span>that a given point is real, adjusting probabilities to assign a value of 1 for real examples and 0 for fake data points.</p><h2 id="how-gans-learn-training-and-backpropagation" class="sc-4555ca6a-0 sc-42818e5a-3 kIEulY ipqlKg"><span font-weight="bold" class="sc-b323b31-0 caUwZV">How GANs Learn: Training and Backpropagation</span></h2><p class="sc-5159831f-0">This learning process for GANs involves adversarial optimization of both the generator and discriminator. In other words, the objective of this process is to find a global optimum for the loss functions of both networks. This optimization achieves a state where the generator effectively reproduces the real data distribution. </p><p class="sc-5159831f-0">The generator seeks to minimize its loss function, which is typically a measure of how easily the discriminator can identify its outputs as fake. Conversely, the discriminator aims to maximize its loss function, reflecting its ability to identify real and generated data correctly.</p><h3 id="the-generators-loss-function" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">The generator's loss function</h3><p class="sc-5159831f-0">Here is the mathematical representation of the generator’s loss function:</p><p class="sc-5159831f-0"><div width="100%" class="sc-ace17a57-0 ldWHw component-img-wrapper" id="LQsb3r78Q9OLfiVYNjfV-A"><div class="sc-ace17a57-0 sc-5f7d928-0 iLvNR kritbo image-wrapper" width="fit-content" height="fit-content" display="flex"><img alt="" title="" loading="lazy" width="1999" height="158" decoding="async" data-nimg="1" class="sc-3ec8aa52-0 jTMTOn desktop-image" style="color:transparent;object-fit:contain;background-size:contain;background-position:50% 50%;background-repeat:no-repeat;background-image:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 1999 158'%3E%3Cfilter id='b' color-interpolation-filters='sRGB'%3E%3CfeGaussianBlur stdDeviation='20'/%3E%3CfeColorMatrix values='1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 100 -1' result='s'/%3E%3CfeFlood x='0' y='0' width='100%25' height='100%25'/%3E%3CfeComposite operator='out' in='s'/%3E%3CfeComposite in2='SourceGraphic'/%3E%3CfeGaussianBlur stdDeviation='20'/%3E%3C/filter%3E%3Cimage width='100%25' height='100%25' x='0' y='0' preserveAspectRatio='none' style='filter: url(%23b);' href='/deepgram.jpg'/%3E%3C/svg%3E")" sizes="(min-width: 992px) 810px, 100vw" srcSet="/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085557-image3-modified.png&w=640&q=75 640w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085557-image3-modified.png&w=750&q=75 750w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085557-image3-modified.png&w=828&q=75 828w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085557-image3-modified.png&w=1080&q=75 1080w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085557-image3-modified.png&w=1200&q=75 1200w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085557-image3-modified.png&w=1920&q=75 1920w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085557-image3-modified.png&w=2048&q=75 2048w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085557-image3-modified.png&w=3840&q=75 3840w" src="/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085557-image3-modified.png&w=3840&q=75"/></div></div></p><p class="sc-5159831f-0"><span font-family="secondaryFont" class="sc-b323b31-0 sc-42818e5a-4 hqcSqI jpHeYz">E</span> denotes the <a href="https://en.wikipedia.org/wiki/Expected_value#:~:text=In%20probability%20theory%2C%20the%20expected,generalization%20of%20the%20weighted%20average."><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">expectation</span></a> over the latent space (z) and the real data distribution (x).</p><p class="sc-5159831f-0">The goal is to <em>minimize</em> the combination of two log-probability terms:</p><ul class="sc-ace17a57-0 sc-42818e5a-1 dslPBO dZzOHq"><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><span font-family="secondaryFont" class="sc-b323b31-0 sc-42818e5a-4 hqcSqI jpHeYz">log(D(G(z))</span> represents the log probability that the discriminator (D) classifies the generated data as real.</p></li></li><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><span font-family="secondaryFont" class="sc-b323b31-0 sc-42818e5a-4 hqcSqI jpHeYz">log(1 - D(x))</span> represents the log probability that the discriminator correctly identifies real data as real.</p></li></li></ul><h4 id="the-discriminators-loss-function" class="sc-4555ca6a-0 sc-42818e5a-3 gSWJsZ ipqlKg">The discriminator's loss function</h4><p class="sc-5159831f-0">Here is the mathematical representation of the discriminator’s loss function:</p><p class="sc-5159831f-0"><div width="100%" class="sc-ace17a57-0 ldWHw component-img-wrapper" id="E4DkGWonT4mSPsfHtAF5ZQ"><div class="sc-ace17a57-0 sc-5f7d928-0 iLvNR kritbo image-wrapper" width="fit-content" height="fit-content" display="flex"><img alt="" title="" loading="lazy" width="1999" height="155" decoding="async" data-nimg="1" class="sc-3ec8aa52-0 jTMTOn desktop-image" style="color:transparent;object-fit:contain;background-size:contain;background-position:50% 50%;background-repeat:no-repeat;background-image:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 1999 155'%3E%3Cfilter id='b' color-interpolation-filters='sRGB'%3E%3CfeGaussianBlur stdDeviation='20'/%3E%3CfeColorMatrix values='1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 100 -1' result='s'/%3E%3CfeFlood x='0' y='0' width='100%25' height='100%25'/%3E%3CfeComposite operator='out' in='s'/%3E%3CfeComposite in2='SourceGraphic'/%3E%3CfeGaussianBlur stdDeviation='20'/%3E%3C/filter%3E%3Cimage width='100%25' height='100%25' x='0' y='0' preserveAspectRatio='none' style='filter: url(%23b);' href='/deepgram.jpg'/%3E%3C/svg%3E")" sizes="(min-width: 992px) 810px, 100vw" srcSet="/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085732-image8-modified.png&w=640&q=75 640w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085732-image8-modified.png&w=750&q=75 750w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085732-image8-modified.png&w=828&q=75 828w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085732-image8-modified.png&w=1080&q=75 1080w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085732-image8-modified.png&w=1200&q=75 1200w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085732-image8-modified.png&w=1920&q=75 1920w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085732-image8-modified.png&w=2048&q=75 2048w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085732-image8-modified.png&w=3840&q=75 3840w" src="/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085732-image8-modified.png&w=3840&q=75"/></div></div></p><p class="sc-5159831f-0">The function consists of two terms: </p><ul class="sc-ace17a57-0 sc-42818e5a-1 dslPBO dZzOHq"><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0"><span font-family="secondaryFont" class="sc-b323b31-0 sc-42818e5a-4 hqcSqI jpHeYz">log(D(G(z)) </span>which measures the probability the discriminator assigns to the generated data, aiming to <em>maximize</em> it.</p></li></li><li class="sc-ace17a57-0 sc-42818e5a-0 PRHwk hegRkn"><li><p class="sc-5159831f-0">The second term, <span font-family="secondaryFont" class="sc-b323b31-0 sc-42818e5a-4 hqcSqI jpHeYz">log(1 - D(x))</span> evaluates the probability assigned by the discriminator to real data, striving to <em>minimize</em> it.</p></li></li></ul><p class="sc-5159831f-0">The overall aim is to find the optimal parameters for the generator that make the generated data indistinguishable from real data.</p><h4 id="unified-training-objective" class="sc-4555ca6a-0 sc-42818e5a-3 gSWJsZ ipqlKg">Unified training objective</h4><p class="sc-5159831f-0"><div width="100%" class="sc-ace17a57-0 ldWHw component-img-wrapper" id="O66IgbhlTgSmSLvq_3kcOg"><div class="sc-ace17a57-0 sc-5f7d928-0 iLvNR kritbo image-wrapper" width="fit-content" height="fit-content" display="flex"><img alt="" title="" loading="lazy" width="1999" height="142" decoding="async" data-nimg="1" class="sc-3ec8aa52-0 jTMTOn desktop-image" style="color:transparent;object-fit:contain;background-size:contain;background-position:50% 50%;background-repeat:no-repeat;background-image:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 1999 142'%3E%3Cfilter id='b' color-interpolation-filters='sRGB'%3E%3CfeGaussianBlur stdDeviation='20'/%3E%3CfeColorMatrix values='1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 100 -1' result='s'/%3E%3CfeFlood x='0' y='0' width='100%25' height='100%25'/%3E%3CfeComposite operator='out' in='s'/%3E%3CfeComposite in2='SourceGraphic'/%3E%3CfeGaussianBlur stdDeviation='20'/%3E%3C/filter%3E%3Cimage width='100%25' height='100%25' x='0' y='0' preserveAspectRatio='none' style='filter: url(%23b);' href='/deepgram.jpg'/%3E%3C/svg%3E")" sizes="(min-width: 992px) 810px, 100vw" srcSet="/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085771-image6-modified.png&w=640&q=75 640w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085771-image6-modified.png&w=750&q=75 750w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085771-image6-modified.png&w=828&q=75 828w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085771-image6-modified.png&w=1080&q=75 1080w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085771-image6-modified.png&w=1200&q=75 1200w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085771-image6-modified.png&w=1920&q=75 1920w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085771-image6-modified.png&w=2048&q=75 2048w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085771-image6-modified.png&w=3840&q=75 3840w" src="/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1703085771-image6-modified.png&w=3840&q=75"/></div></div></p><p class="sc-5159831f-0">Combining the loss functions of each network, the unified training objective formula shows the essence of the entire training process. It emphasizes minimizing the generator's loss and maximizing the discriminator's loss, which brings both networks to an optimum point.</p><h3 id="backpropagation-in-gans" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">Backpropagation in GANs</h3><p class="sc-5159831f-0">Backpropagation in the network calculates the gradients of the loss functions, enabling the model to adjust its parameters and progressively improve performance. Typically, it involves adjusting errors backwards through the network, which helps refine both the generator and discriminator networks by minimizing their losses.</p><h3 id="evaluating-gan-performance" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">Evaluating GAN Performance</h3><p class="sc-5159831f-0">Evaluating the performance of GANs is more complex than assessing traditional machine learning models. You assess generative models using a combination of metrics that address different aspects of the generated output. Some metrics, like the <a href="https://arxiv.org/pdf/1801.01973.pdf#:~:text=The%20Inception%20Score%20is%20a,from%20the%20CIFAR%2D10%20dataset."><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Inception Score (IS)</span></a>, <a href="https://en.wikipedia.org/wiki/Fr%C3%A9chet_inception_distance"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Frechet Inception Distance (FID)</span></a>, Human Evaluation, <a href="https://en.wikipedia.org/wiki/Precision_and_recall"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Precision, Recall</span></a>, and <a href="https://en.wikipedia.org/wiki/F-score"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">F1-score</span></a>, are combined to evaluate GAN performance based on the task it is used for.</p><p class="sc-5159831f-0">Generally, these metrics evaluate different aspects of performance, such as image quality, diversity, similarity to real data distribution, and the ability to deceive a discriminator. The ultimate goal is to ensure that the generator produces high-quality, diverse, and realistic samples.</p><h2 id="gans-in-action-applications-in-computer-vision" class="sc-4555ca6a-0 sc-42818e5a-3 kIEulY ipqlKg"><span font-weight="bold" class="sc-b323b31-0 caUwZV">GANs in Action: Applications in Computer Vision</span></h2><p class="sc-5159831f-0">GANs come to life in computer vision with transformative applications ranging from image synthesis to object detection. Here are a few applications of GANs.</p><h3 id="image-generation-and-enhancement" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">Image generation and enhancement</h3><p class="sc-5159831f-0">Models like StyleGAN, BigGAN, and other conditioned GANs have demonstrated great proficiency in generating realistic, high-resolution images or sketches. Their ability to capture fine details in images and produce diverse outputs makes them useful for various tasks, from artistic image creation to generating synthetic datasets for training ML models.</p><h3 id="data-augmentation-for-machine-learning" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">Data augmentation for machine learning</h3><p class="sc-5159831f-0"><a href="https://en.wikipedia.org/wiki/Data_augmentation"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Data augmentation</span></a> improves model performance in computer vision tasks and ML by diversifying the training dataset. When realistic variations of existing datasets are created with GANs for a computer vision task, it can enhance the robustness and generalization strength of the model, reducing the chances of overfitting. This is particularly valuable in fields where data is limited and challenging to collect.</p><p class="sc-5159831f-0"><div width="100%" class="sc-ace17a57-0 ldWHw component-img-wrapper" id="AGJOfobER-azW9kVyt5ZiA"><div class="sc-ace17a57-0 sc-5f7d928-0 iLvNR kritbo image-wrapper" width="fit-content" height="fit-content" display="flex"><img alt="" title="" loading="lazy" width="1796" height="1088" decoding="async" data-nimg="1" class="sc-3ec8aa52-0 jTMTOn desktop-image" style="color:transparent;object-fit:contain;background-size:contain;background-position:50% 50%;background-repeat:no-repeat;background-image:url("data:image/svg+xml;charset=utf-8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 1796 1088'%3E%3Cfilter id='b' color-interpolation-filters='sRGB'%3E%3CfeGaussianBlur stdDeviation='20'/%3E%3CfeColorMatrix values='1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 100 -1' result='s'/%3E%3CfeFlood x='0' y='0' width='100%25' height='100%25'/%3E%3CfeComposite operator='out' in='s'/%3E%3CfeComposite in2='SourceGraphic'/%3E%3CfeGaussianBlur stdDeviation='20'/%3E%3C/filter%3E%3Cimage width='100%25' height='100%25' x='0' y='0' preserveAspectRatio='none' style='filter: url(%23b);' href='/deepgram.jpg'/%3E%3C/svg%3E")" sizes="(min-width: 992px) 810px, 100vw" srcSet="/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1718736518-screen-shot-2024-06-18-at-11-48-34-am.png&w=640&q=75 640w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1718736518-screen-shot-2024-06-18-at-11-48-34-am.png&w=750&q=75 750w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1718736518-screen-shot-2024-06-18-at-11-48-34-am.png&w=828&q=75 828w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1718736518-screen-shot-2024-06-18-at-11-48-34-am.png&w=1080&q=75 1080w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1718736518-screen-shot-2024-06-18-at-11-48-34-am.png&w=1200&q=75 1200w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1718736518-screen-shot-2024-06-18-at-11-48-34-am.png&w=1920&q=75 1920w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1718736518-screen-shot-2024-06-18-at-11-48-34-am.png&w=2048&q=75 2048w, /_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1718736518-screen-shot-2024-06-18-at-11-48-34-am.png&w=3840&q=75 3840w" src="/_next/image?url=https%3A%2F%2Fwww.datocms-assets.com%2F96965%2F1718736518-screen-shot-2024-06-18-at-11-48-34-am.png&w=3840&q=75"/></div><div font-size="textSm" font-style="italic" class="sc-6293d692-0 jgEMAk"></div></div></p><h3 id="realworld-examples-and-case-studies" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">Real-world examples and case studies</h3><p class="sc-5159831f-0"><a href="https://arxiv.org/abs/1703.10593"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">CycleGAN</span></a> facilitates the <a href="https://arxiv.org/pdf/1903.03374.pdf"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">translation of images in medical imaging</span></a> across different modalities, aiding in diagnostic tasks or <a href="https://ieeexplore.ieee.org/document/8553236"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">voice conversion</span></a>. GANs are also used for <a href="https://arxiv.org/abs/1805.07112"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">image captioning</span></a> or video synthesis, allowing the generation of descriptive captions for images and the creation of lifelike video sequences. These technologies have found significant use in virtual environments and simulations, among other applications.</p><h2 id="overcoming-the-challenges-in-gans" class="sc-4555ca6a-0 sc-42818e5a-3 kIEulY ipqlKg"><span font-weight="bold" class="sc-b323b31-0 caUwZV">Overcoming the Challenges in GANs</span></h2><p class="sc-5159831f-0">Despite their numerous applications and advancements, GANs face several challenges.</p><h3 id="stability-and-convergence-issues" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">Stability and convergence issues</h3><p class="sc-5159831f-0">When training GANs, stability and convergence issues (the adversarial networks have difficulties reaching a stable and desired state) are usually a concern. Techniques like <a href="https://arxiv.org/abs/1802.05957"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">spectral normalization</span></a> and <a href="https://arxiv.org/abs/1710.10196"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">progressive growing</span></a> have stabilised the training process and addressed these issues. Different optimization techniques (like utilizing different learning rates for the generator and discriminator) can contribute to more stable and reliable training.</p><h3 id="mode-collapse-and-diversity" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">Mode collapse and diversity</h3><p class="sc-5159831f-0"><a href="https://en.wikipedia.org/wiki/Generative_adversarial_network"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Mode collapse</span></a> occurs when a GAN fails to capture the full variability of the training data, leading to repetitive or limited outputs. Addressing this challenge involves incorporating <a href="https://paperswithcode.com/method/minibatch-discrimination"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">minibatch discrimination</span></a> or <a href="https://arxiv.org/abs/1910.12027"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">consistency regularization</span></a> to make the generated samples more consistent. Using progressive growing and conditional GANs has proven helpful in ensuring the generated samples are varied and look real. It helps avoid generator mode collapse.</p><h3 id="ethical-considerations-and-misuse-prevention" class="sc-4555ca6a-0 sc-42818e5a-3 cGaypB ipqlKg">Ethical considerations and misuse prevention</h3><p class="sc-5159831f-0">Concerns about GAN technology, especially in making <a href="https://en.wikipedia.org/wiki/Deepfake"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Deepfakes</span></a>, have been seen within the research community and mainstream media. Efforts are underway to develop better methods for detecting Deepfakes and making AI more transparent. <a href="https://en.wikipedia.org/wiki/Ethics_of_artificial_intelligence"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Responsible AI</span></a> efforts and ethical guidelines are committed to reducing the potential harms of GAN misuse, like spreading misinformation and privacy breaches.</p><h2 id="the-future-of-gans-beyond-image-generation" class="sc-4555ca6a-0 sc-42818e5a-3 kIEulY ipqlKg"><span font-weight="bold" class="sc-b323b31-0 caUwZV">The Future of GANs: Beyond Image Generation</span></h2><p class="sc-5159831f-0">GANs have come a long way with lots of improvements, giving us different versions like Wasserstein GAN, which helps with stability and reduces problems like mode collapse; conditional GAN, which is task-specific and CycleGAN (great for speech or audio conversions); <a href="https://paperswithcode.com/method/specgan"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">SpecGAN</span></a>, <a href="https://arxiv.org/abs/1802.04208"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">WaveGAN</span></a>, and <a href="https://arxiv.org/abs/1902.08710"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">GANsynth</span></a>, which can be used to generate spectrographs and audio, among other innovations. Researchers are also exploring how GANs can work with new <a href="https://deepgram.com/ai-glossary/deep-learning">deep learning</a> tools like <a href="https://deepgram.com/ai-glossary/transformers"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Transformers</span></a>, <a href="https://arxiv.org/abs/1811.02033"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Physics-Informed Neural Networks</span></a>, <a href="https://deepgram.com/ai-glossary/large-language-model"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Large Language models</span></a>, and <a href="https://arxiv.org/abs/2206.02262"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">Diffusion models</span></a>.<br/><br/>GANs have moved beyond their initial image-related focus and are now used in tasks like text-to-image synthesis, image-to-image translation, <a href="https://arxiv.org/abs/2107.11098"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">time series</span></a>, and <a href="https://arxiv.org/ftp/arxiv/papers/2104/2104.09917.pdf"><span text-decoration="underline" class="sc-b323b31-0 cLUpGM">semantic segmentation</span></a>. These and many more advancements exemplify the ever-evolving research on GANs. 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href="/ai-glossary/grounding" class="sc-e69ac761-0 kmKRMV">Grounding</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/feedforward-neural-network" class="sc-e69ac761-0 kmKRMV">Feedforward Neural Network</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/bert" class="sc-e69ac761-0 kmKRMV">BERT</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/gradient-boosting-machines" class="sc-e69ac761-0 kmKRMV">Gradient Boosting Machines (GBMs)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/retrieval-augmented-generation" class="sc-e69ac761-0 kmKRMV">Retrieval-Augmented Generation (RAG)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/perceptron" class="sc-e69ac761-0 kmKRMV">Perceptron</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/overfitting-underfitting" class="sc-e69ac761-0 kmKRMV">Overfitting and Underfitting</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning" class="sc-e69ac761-0 kmKRMV">Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/large-language-model" class="sc-e69ac761-0 kmKRMV">Large Language Model (LLM)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/graphics-processing-unit-gpu" class="sc-e69ac761-0 kmKRMV">Graphics Processing Unit (GPU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/diffusion-model" class="sc-e69ac761-0 kmKRMV">Diffusion Models</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/classification" class="sc-e69ac761-0 kmKRMV">Classification</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/tensor-processing-unit-tpu" class="sc-e69ac761-0 kmKRMV">Tensor Processing Unit (TPU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-processing" class="sc-e69ac761-0 kmKRMV">Natural Language Processing (NLP)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/google-bard" class="sc-e69ac761-0 kmKRMV">Google's Bard</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/openai-whisper" class="sc-e69ac761-0 kmKRMV">OpenAI Whisper</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/sequence-modeling" class="sc-e69ac761-0 kmKRMV">Sequence Modeling</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/precision-and-recall" class="sc-e69ac761-0 kmKRMV">Precision and Recall</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/semantic-kernel" class="sc-e69ac761-0 kmKRMV">Semantic Kernel</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/fine-tuning-deep-learning" class="sc-e69ac761-0 kmKRMV">Fine Tuning in Deep Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/gradient-scaling" class="sc-e69ac761-0 kmKRMV">Gradient Scaling</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/alphago-zero" class="sc-e69ac761-0 kmKRMV">AlphaGo Zero</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/cognitive-map" class="sc-e69ac761-0 kmKRMV">Cognitive Map</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/keyphrase-extraction" class="sc-e69ac761-0 kmKRMV">Keyphrase Extraction</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/multimodal-aI-models-and-modalities" class="sc-e69ac761-0 kmKRMV">Multimodal AI Models and Modalities</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/hidden-markov-models" class="sc-e69ac761-0 kmKRMV">Hidden Markov Models (HMMs)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-hardware" class="sc-e69ac761-0 kmKRMV">AI Hardware</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/deep-learning" class="sc-e69ac761-0 kmKRMV">Deep Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-generation" class="sc-e69ac761-0 kmKRMV">Natural Language Generation (NLG)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-understanding" class="sc-e69ac761-0 kmKRMV">Natural Language Understanding (NLU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/tokenization" class="sc-e69ac761-0 kmKRMV">Tokenization</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/word-embeddings" class="sc-e69ac761-0 kmKRMV">Word Embeddings</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-and-finance" class="sc-e69ac761-0 kmKRMV">AI and Finance</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/alphago" class="sc-e69ac761-0 kmKRMV">AlphaGo</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-recommendation-algorithms" class="sc-e69ac761-0 kmKRMV">AI Recommendation Algorithms</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/binary-classification-ai" class="sc-e69ac761-0 kmKRMV">Binary Classification AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-generated-music" class="sc-e69ac761-0 kmKRMV">AI Generated Music</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/neuralink" class="sc-e69ac761-0 kmKRMV">Neuralink</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-video-generation" class="sc-e69ac761-0 kmKRMV">AI Video Generation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/openai-sora" class="sc-e69ac761-0 kmKRMV">OpenAI Sora</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/hooke-jeeves-algorithm" class="sc-e69ac761-0 kmKRMV">Hooke-Jeeves Algorithm</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/mamba" class="sc-e69ac761-0 kmKRMV">Mamba</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/central-processing-unit-cpu" class="sc-e69ac761-0 kmKRMV">Central Processing Unit (CPU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/generative-ai" class="sc-e69ac761-0 kmKRMV">Generative AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/representation-learning" class="sc-e69ac761-0 kmKRMV">Representation Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-in-customer-service" class="sc-e69ac761-0 kmKRMV">AI in Customer Service</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/conditional-variational-autoencoders" class="sc-e69ac761-0 kmKRMV">Conditional Variational Autoencoders</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/conversational-ai" class="sc-e69ac761-0 kmKRMV">Conversational AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/packages-glossary" class="sc-e69ac761-0 kmKRMV">Packages</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/models" class="sc-e69ac761-0 kmKRMV">Models</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/fundamentals" class="sc-e69ac761-0 kmKRMV">Fundamentals</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/datasets" class="sc-e69ac761-0 kmKRMV">Datasets</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/techniques" class="sc-e69ac761-0 kmKRMV">Techniques</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-lifecycle-management" class="sc-e69ac761-0 kmKRMV">AI Lifecycle Management</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-literacy" class="sc-e69ac761-0 kmKRMV">AI Literacy</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-monitoring" class="sc-e69ac761-0 kmKRMV">AI Monitoring</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-oversight" class="sc-e69ac761-0 kmKRMV">AI Oversight</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-privacy" class="sc-e69ac761-0 kmKRMV">AI Privacy</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-prototyping" class="sc-e69ac761-0 kmKRMV">AI Prototyping</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-regulation" class="sc-e69ac761-0 kmKRMV">AI Regulation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-resilience" class="sc-e69ac761-0 kmKRMV">AI Resilience</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning-bias" class="sc-e69ac761-0 kmKRMV">Machine Learning Bias</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning-life-cycle-management" class="sc-e69ac761-0 kmKRMV">Machine Learning Life Cycle Management</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-translation" class="sc-e69ac761-0 kmKRMV">Machine Translation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/mlops" class="sc-e69ac761-0 kmKRMV">MLOps</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/monte-carlo-learning" class="sc-e69ac761-0 kmKRMV">Monte Carlo Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/multi-task-learning" class="sc-e69ac761-0 kmKRMV">Multi-task Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/naive-bayes-classifier" class="sc-e69ac761-0 kmKRMV">Naive Bayes Classifier</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning-neuron" class="sc-e69ac761-0 kmKRMV">Machine Learning Neuron</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/pooling-machine-learning" class="sc-e69ac761-0 kmKRMV">Pooling (Machine Learning)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/principal-component-analysis" class="sc-e69ac761-0 kmKRMV">Principal Component Analysis</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning-preprocessing" class="sc-e69ac761-0 kmKRMV">Machine Learning Preprocessing</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/rectified-linear-unit-relu" class="sc-e69ac761-0 kmKRMV">Rectified Linear Unit (ReLU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/reproducibility-machine-learning" class="sc-e69ac761-0 kmKRMV">Reproducibility in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/restricted-boltzmann-machines" class="sc-e69ac761-0 kmKRMV">Restricted Boltzmann Machines</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/semi-supervised-learning" class="sc-e69ac761-0 kmKRMV">Semi-Supervised Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/supervised-learning" class="sc-e69ac761-0 kmKRMV">Supervised Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/support-vector-machines-svm" class="sc-e69ac761-0 kmKRMV">Support Vector Machines (SVM)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/topic-modeling" class="sc-e69ac761-0 kmKRMV">Topic Modeling</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/uncertainty-machine-learning" class="sc-e69ac761-0 kmKRMV">Uncertainty in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/vanishing-and-exploding-gradients" class="sc-e69ac761-0 kmKRMV">Vanishing and Exploding Gradients</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-interpretability" class="sc-e69ac761-0 kmKRMV">AI Interpretability</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/data-labeling" class="sc-e69ac761-0 kmKRMV">Data Labeling</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/inference-engine" class="sc-e69ac761-0 kmKRMV">Inference Engine</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/probabilistic-models-in-machine-learning" class="sc-e69ac761-0 kmKRMV">Probabilistic Models in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/f1-score-machine-learning" class="sc-e69ac761-0 kmKRMV">F1 Score in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/expectation-maximization" class="sc-e69ac761-0 kmKRMV">Expectation Maximization</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/beam-search-algorithm" class="sc-e69ac761-0 kmKRMV">Beam Search Algorithm</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/embedding-layer" class="sc-e69ac761-0 kmKRMV">Embedding Layer</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/differential-privacy" class="sc-e69ac761-0 kmKRMV">Differential Privacy</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/data-poisoning" class="sc-e69ac761-0 kmKRMV">Data Poisoning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/causal-inference" class="sc-e69ac761-0 kmKRMV">Causal Inference</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/capsule-neural-network" class="sc-e69ac761-0 kmKRMV">Capsule Neural Network</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/attention-mechanisms" class="sc-e69ac761-0 kmKRMV">Attention Mechanisms</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/domain-adaptation" class="sc-e69ac761-0 kmKRMV">Domain Adaptation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/evolutionary-algorithms" class="sc-e69ac761-0 kmKRMV">Evolutionary Algorithms</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/contrastive-learning" class="sc-e69ac761-0 kmKRMV">Contrastive Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/explainable-ai" class="sc-e69ac761-0 kmKRMV">Explainable AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/affective-ai" class="sc-e69ac761-0 kmKRMV">Affective AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/semantic-networks" class="sc-e69ac761-0 kmKRMV">Semantic Networks</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/data-augmentation" class="sc-e69ac761-0 kmKRMV">Data Augmentation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/convolutional-neural-networks" class="sc-e69ac761-0 kmKRMV">Convolutional Neural Networks</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/cognitive-computing" class="sc-e69ac761-0 kmKRMV">Cognitive Computing</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/end-to-end-learning" class="sc-e69ac761-0 kmKRMV">End-to-end Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/prompt-tuning" class="sc-e69ac761-0 kmKRMV">Prompt Tuning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/double-descent" class="sc-e69ac761-0 kmKRMV">Double Descent</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/model-drift" class="sc-e69ac761-0 kmKRMV">Model Drift</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/neural-radiance-fields" class="sc-e69ac761-0 kmKRMV">Neural Radiance Fields</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/regularization" class="sc-e69ac761-0 kmKRMV">Regularization</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-querying" class="sc-e69ac761-0 kmKRMV">Natural Language Querying (NLQ)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/foundation-models" class="sc-e69ac761-0 kmKRMV">Foundation Models</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/forward-propagation" class="sc-e69ac761-0 kmKRMV">Forward Propagation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/f2-score" class="sc-e69ac761-0 kmKRMV">F2 Score</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-ethics" class="sc-e69ac761-0 kmKRMV">AI Ethics</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/transfer-learning" class="sc-e69ac761-0 kmKRMV">Transfer Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-alignment" class="sc-e69ac761-0 kmKRMV">AI Alignment</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/whisper-v3" class="sc-e69ac761-0 kmKRMV">Whisper v3</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/whisper-v2" class="sc-e69ac761-0 kmKRMV">Whisper v2</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/semi-structured-data" class="sc-e69ac761-0 kmKRMV">Semi-structured data</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ai-hallucinations" class="sc-e69ac761-0 kmKRMV">AI Hallucinations</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/emergent-behavior" class="sc-e69ac761-0 kmKRMV">Emergent Behavior</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/matplotlib" class="sc-e69ac761-0 kmKRMV">Matplotlib</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/numpy" class="sc-e69ac761-0 kmKRMV">NumPy</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/scikit-learn" class="sc-e69ac761-0 kmKRMV">Scikit-learn</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/scipy" class="sc-e69ac761-0 kmKRMV">SciPy</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/keras" class="sc-e69ac761-0 kmKRMV">Keras</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/tensorflow" class="sc-e69ac761-0 kmKRMV">TensorFlow</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/seaborn-python-package" class="sc-e69ac761-0 kmKRMV">Seaborn Python Package</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/pytorch" class="sc-e69ac761-0 kmKRMV">PyTorch</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-toolkit-nltk" class="sc-e69ac761-0 kmKRMV">Natural Language Toolkit (NLTK)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/pandas" class="sc-e69ac761-0 kmKRMV">Pandas</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ego-4d" class="sc-e69ac761-0 kmKRMV">Ego 4D</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/the-pile" 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print{.css-ak4akz{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ak4akz:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-ak4akz:hover{background-color:transparent;}}.css-ak4akz.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-ak4akz::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ak4akz:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-ak4akz:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-ak4akz.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><button class="MuiButtonBase-root 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-q0kznr:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-q0kznr:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-q0kznr.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><style data-emotion="css 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-ak4akz{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ak4akz:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-ak4akz:hover{background-color:transparent;}}.css-ak4akz.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-ak4akz::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ak4akz:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-ak4akz:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-ak4akz.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><button class="MuiButtonBase-root 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-q0kznr:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-q0kznr:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-q0kznr.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><style data-emotion="css ak4akz">.css-ak4akz{display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;position:relative;box-sizing:border-box;-webkit-tap-highlight-color:transparent;background-color:transparent;outline:0;border:0;margin:0;border-radius:0;padding:0;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;vertical-align:middle;-moz-appearance:none;-webkit-appearance:none;-webkit-text-decoration:none;text-decoration:none;color:inherit;font-family:var(--font-inter, sans-serif);font-size:0.875rem;line-height:1.75rem;font-weight:700;text-transform:uppercase;min-width:64px;padding:6px 8px;border-radius:4px;-webkit-transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-ak4akz{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ak4akz:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-ak4akz:hover{background-color:transparent;}}.css-ak4akz.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-ak4akz::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ak4akz:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-ak4akz:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-ak4akz.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><button class="MuiButtonBase-root 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-q0kznr:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-q0kznr:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-q0kznr.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><style data-emotion="css ak4akz">.css-ak4akz{display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;position:relative;box-sizing:border-box;-webkit-tap-highlight-color:transparent;background-color:transparent;outline:0;border:0;margin:0;border-radius:0;padding:0;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;vertical-align:middle;-moz-appearance:none;-webkit-appearance:none;-webkit-text-decoration:none;text-decoration:none;color:inherit;font-family:var(--font-inter, sans-serif);font-size:0.875rem;line-height:1.75rem;font-weight:700;text-transform:uppercase;min-width:64px;padding:6px 8px;border-radius:4px;-webkit-transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-ak4akz{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ak4akz:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-ak4akz:hover{background-color:transparent;}}.css-ak4akz.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-ak4akz::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ak4akz:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-ak4akz:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-ak4akz.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><button class="MuiButtonBase-root 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-ak4akz{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ak4akz:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-ak4akz:hover{background-color:transparent;}}.css-ak4akz.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-ak4akz::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ak4akz:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-ak4akz:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-ak4akz.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><button class="MuiButtonBase-root 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-q0kznr:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-q0kznr:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-q0kznr.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><style data-emotion="css 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-ak4akz{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ak4akz:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-ak4akz:hover{background-color:transparent;}}.css-ak4akz.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-ak4akz::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ak4akz:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-ak4akz:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-ak4akz.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><button class="MuiButtonBase-root 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-q0kznr:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-q0kznr:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-q0kznr.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><style data-emotion="css ak4akz">.css-ak4akz{display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;position:relative;box-sizing:border-box;-webkit-tap-highlight-color:transparent;background-color:transparent;outline:0;border:0;margin:0;border-radius:0;padding:0;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;vertical-align:middle;-moz-appearance:none;-webkit-appearance:none;-webkit-text-decoration:none;text-decoration:none;color:inherit;font-family:var(--font-inter, sans-serif);font-size:0.875rem;line-height:1.75rem;font-weight:700;text-transform:uppercase;min-width:64px;padding:6px 8px;border-radius:4px;-webkit-transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-ak4akz{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ak4akz:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-ak4akz:hover{background-color:transparent;}}.css-ak4akz.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-ak4akz::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ak4akz:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-ak4akz:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-ak4akz.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><button class="MuiButtonBase-root 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-q0kznr:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-q0kznr:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-q0kznr.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><style data-emotion="css 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-ak4akz{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ak4akz:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-ak4akz:hover{background-color:transparent;}}.css-ak4akz.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-ak4akz::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ak4akz:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-ak4akz:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-ak4akz.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><button class="MuiButtonBase-root 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media 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0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-q0kznr:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-q0kznr:hover{background-color:transparent;}}.css-q0kznr.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-q0kznr::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms 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ak4akz">.css-ak4akz{display:-webkit-inline-box;display:-webkit-inline-flex;display:-ms-inline-flexbox;display:inline-flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;position:relative;box-sizing:border-box;-webkit-tap-highlight-color:transparent;background-color:transparent;outline:0;border:0;margin:0;border-radius:0;padding:0;cursor:pointer;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;vertical-align:middle;-moz-appearance:none;-webkit-appearance:none;-webkit-text-decoration:none;text-decoration:none;color:inherit;font-family:var(--font-inter, sans-serif);font-size:0.875rem;line-height:1.75rem;font-weight:700;text-transform:uppercase;min-width:64px;padding:6px 8px;border-radius:4px;-webkit-transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;transition:background-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,box-shadow 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,border-color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms,color 250ms cubic-bezier(0.4, 0, 0.2, 1) 0ms;color:#00E062;min-width:0;text-transform:capitalize;font-weight:600;border-radius:4px;font-size:1rem;padding-left:2rem;padding-right:2rem;-webkit-transition:unset;transition:unset;white-space:nowrap;position:relative;font-size:1rem;line-height:2;padding-left:1.25rem;padding-right:1.25rem;color:#79AFFA;padding:0;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}.css-ak4akz::-moz-focus-inner{border-style:none;}.css-ak4akz.Mui-disabled{pointer-events:none;cursor:default;}@media print{.css-ak4akz{-webkit-print-color-adjust:exact;color-adjust:exact;}}.css-ak4akz:hover{-webkit-text-decoration:none;text-decoration:none;background-color:rgba(0, 224, 98, 0.04);}@media (hover: none){.css-ak4akz:hover{background-color:transparent;}}.css-ak4akz.Mui-disabled{color:rgba(0, 0, 0, 0.26);}.css-ak4akz::before{content:"";position:absolute;width:100%;height:100%;top:-1px;left:-1px;z-index:0;opacity:0;-webkit-transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;transition:opacity 100ms ease-in-out,background-position 400ms ease-in-out;border-radius:4px;box-sizing:content-box;padding:1px;}.css-ak4akz:hover{color:#ffffff;-webkit-text-decoration:underline;text-decoration:underline;background-color:transparent;}.css-ak4akz:focus-visible{color:#ffffff;-webkit-text-decoration:none;text-decoration:none;border-radius:4px;outline:2px solid #687EF7;outline-offset:3px;}.css-ak4akz.Mui-disabled{color:#96A2FF;opacity:0.5;}</style><button class="MuiButtonBase-root MuiButton-root MuiButton-text MuiButton-textPrimary MuiButton-sizeMedium MuiButton-textSizeMedium MuiButton-colorPrimary MuiButton-root MuiButton-text MuiButton-textPrimary MuiButton-sizeMedium MuiButton-textSizeMedium MuiButton-colorPrimary sc-d74559df-0 iotBFW css-ak4akz" tabindex="0" type="button" href="" target="_self"><span class="sc-b323b31-0 btXZGI button-label" display="inline-flex">Z</span></button></li></ul></div><div class="sc-ace17a57-0 sc-8e7440ad-1 kpvNPn cxZbdr"><div data-letter="#" class="sc-ace17a57-0 hrNfIL"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 lnNnlT">AI Glossary Categories</span><ul display="none" class="sc-ace17a57-0 itDcJo"><button width="fit-content" cursor="pointer" height="32" class="sc-ace17a57-0 drAJUB"><span display="inline-flex" class="sc-b323b31-0 dKGACE"><svg width="14" height="14" fill="gray-200" stroke="gray-200"><use href="/svgs/icons-updated.svg#plus-icon=solid"></use></svg></span><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jBPWIy">Datasets</span></button><button width="fit-content" cursor="pointer" height="32" class="sc-ace17a57-0 drAJUB"><span display="inline-flex" class="sc-b323b31-0 dKGACE"><svg width="14" height="14" fill="gray-200" stroke="gray-200"><use href="/svgs/icons-updated.svg#plus-icon=solid"></use></svg></span><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jBPWIy">Fundamentals</span></button><button width="fit-content" cursor="pointer" height="32" class="sc-ace17a57-0 drAJUB"><span display="inline-flex" class="sc-b323b31-0 dKGACE"><svg width="14" height="14" fill="gray-200" stroke="gray-200"><use href="/svgs/icons-updated.svg#plus-icon=solid"></use></svg></span><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jBPWIy">Models</span></button><button width="fit-content" cursor="pointer" height="32" class="sc-ace17a57-0 drAJUB"><span display="inline-flex" class="sc-b323b31-0 dKGACE"><svg width="14" height="14" 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href="/ai-glossary/batch-gradient-descent" class="sc-e69ac761-0 dsCZKx">Batch Gradient Descent</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/backpropagation" class="sc-e69ac761-0 dsCZKx">Backpropagation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/bayesian-machine-learning" class="sc-e69ac761-0 dsCZKx">Bayesian Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/bert" class="sc-e69ac761-0 dsCZKx">BERT</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/binary-classification-ai" class="sc-e69ac761-0 dsCZKx">Binary Classification AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/beam-search-algorithm" class="sc-e69ac761-0 dsCZKx">Beam Search Algorithm</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/bias-variance-tradeoff" class="sc-e69ac761-0 dsCZKx">Bias-Variance Tradeoff</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/boosting" class="sc-e69ac761-0 dsCZKx">Boosting in Machine Learning</a></li><li data-letter="C" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">C</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/chess-bots" class="sc-e69ac761-0 dsCZKx">Chess bots</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/chatgpt" class="sc-e69ac761-0 dsCZKx">ChatGPT</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/cure-algorithm" class="sc-e69ac761-0 dsCZKx">CURE Algorithm</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/curse-of-dimensionality" class="sc-e69ac761-0 dsCZKx">Curse of Dimensionality</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/classification" class="sc-e69ac761-0 dsCZKx">Classification</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/cognitive-map" class="sc-e69ac761-0 dsCZKx">Cognitive Map</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/central-processing-unit-cpu" class="sc-e69ac761-0 dsCZKx">Central Processing Unit (CPU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/conditional-variational-autoencoders" class="sc-e69ac761-0 dsCZKx">Conditional Variational Autoencoders</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/conversational-ai" class="sc-e69ac761-0 dsCZKx">Conversational AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/causal-inference" class="sc-e69ac761-0 dsCZKx">Causal Inference</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/capsule-neural-network" class="sc-e69ac761-0 dsCZKx">Capsule Neural Network</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/contrastive-learning" class="sc-e69ac761-0 dsCZKx">Contrastive Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/convolutional-neural-networks" class="sc-e69ac761-0 dsCZKx">Convolutional Neural Networks</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/cognitive-computing" class="sc-e69ac761-0 dsCZKx">Cognitive Computing</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/common-crawl-datasets" class="sc-e69ac761-0 dsCZKx">Common Crawl Datasets</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/clustering-algorithms" class="sc-e69ac761-0 dsCZKx">Clustering Algorithms</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/concatenative-synthesis" class="sc-e69ac761-0 dsCZKx">Concatenative Synthesis</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/candidate-sampling" class="sc-e69ac761-0 dsCZKx">Candidate Sampling</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/computational-creativity" class="sc-e69ac761-0 dsCZKx">Computational Creativity</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/context-aware-computing" class="sc-e69ac761-0 dsCZKx">Context-Aware Computing</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/counterfactual-explanations-in-ai" class="sc-e69ac761-0 dsCZKx">Counterfactual Explanations in AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/cognitive-architectures" class="sc-e69ac761-0 dsCZKx">Cognitive Architectures</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/computational-phenotyping" class="sc-e69ac761-0 dsCZKx">Computational Phenotyping</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/continuous-learning-systems" class="sc-e69ac761-0 dsCZKx">Continuous Learning Systems</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/chatbots" class="sc-e69ac761-0 dsCZKx">Chatbots</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/composite-ai" class="sc-e69ac761-0 dsCZKx">Composite AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/computational-lingustics" class="sc-e69ac761-0 dsCZKx">Computational Linguistics</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/computational-semantics" class="sc-e69ac761-0 dsCZKx">Computational Semantics</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/collaborative-filtering" class="sc-e69ac761-0 dsCZKx">Collaborative Filtering</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/corpus" class="sc-e69ac761-0 dsCZKx">Corpus in NLP</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/confirmation-bias" class="sc-e69ac761-0 dsCZKx">Confirmation Bias in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/confidence-intervals" class="sc-e69ac761-0 dsCZKx">Confidence Intervals in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/cross-validation" class="sc-e69ac761-0 dsCZKx">Cross Validation in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/clustering" class="sc-e69ac761-0 dsCZKx">Clustering in Machine Learning</a></li><li data-letter="D" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">D</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/dall-e" class="sc-e69ac761-0 dsCZKx">Dall-E</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/distilbert" class="sc-e69ac761-0 dsCZKx">DistilBERT</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/dimensionality-reduction" class="sc-e69ac761-0 dsCZKx">Dimensionality Reduction</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/deep-reinforcement-learning" class="sc-e69ac761-0 dsCZKx">Deep Reinforcement Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/diffusion-model" class="sc-e69ac761-0 dsCZKx">Diffusion Models</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/deep-learning" class="sc-e69ac761-0 dsCZKx">Deep Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/datasets" class="sc-e69ac761-0 dsCZKx">Datasets</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/data-labeling" class="sc-e69ac761-0 dsCZKx">Data Labeling</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/differential-privacy" class="sc-e69ac761-0 dsCZKx">Differential Privacy</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/data-poisoning" class="sc-e69ac761-0 dsCZKx">Data Poisoning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/domain-adaptation" class="sc-e69ac761-0 dsCZKx">Domain Adaptation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/data-augmentation" class="sc-e69ac761-0 dsCZKx">Data Augmentation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/double-descent" class="sc-e69ac761-0 dsCZKx">Double Descent</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/deepfake-detection" class="sc-e69ac761-0 dsCZKx">Deepfake Detection</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/diffusion" class="sc-e69ac761-0 dsCZKx">Diffusion</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/data-drift" class="sc-e69ac761-0 dsCZKx">Data Drift</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/data-scarcity" class="sc-e69ac761-0 dsCZKx">Data Scarcity</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/decision-tree" class="sc-e69ac761-0 dsCZKx">Decision Tree</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/decision-intelligence" class="sc-e69ac761-0 dsCZKx">Decision Intelligence</a></li><li data-letter="E" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">E</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/expectation-maximization" class="sc-e69ac761-0 dsCZKx">Expectation Maximization</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/embedding-layer" class="sc-e69ac761-0 dsCZKx">Embedding Layer</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/evolutionary-algorithms" class="sc-e69ac761-0 dsCZKx">Evolutionary Algorithms</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/explainable-ai" class="sc-e69ac761-0 dsCZKx">Explainable AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/end-to-end-learning" class="sc-e69ac761-0 dsCZKx">End-to-end Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/emergent-behavior" class="sc-e69ac761-0 dsCZKx">Emergent Behavior</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ego-4d" class="sc-e69ac761-0 dsCZKx">Ego 4D</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/eco-friendly-ai" class="sc-e69ac761-0 dsCZKx">Eco-friendly AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ensemble-learning" class="sc-e69ac761-0 dsCZKx">Ensemble Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/entropy" class="sc-e69ac761-0 dsCZKx">Entropy in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/epoch" class="sc-e69ac761-0 dsCZKx">Epoch in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ethical-ai" class="sc-e69ac761-0 dsCZKx">Ethical AI</a></li><li data-letter="F" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">F</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/flajolet-martin-algorithm" class="sc-e69ac761-0 dsCZKx">Flajolet-Martin Algorithm</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/feedforward-neural-network" class="sc-e69ac761-0 dsCZKx">Feedforward Neural Network</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/fine-tuning-deep-learning" class="sc-e69ac761-0 dsCZKx">Fine Tuning in Deep Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/fundamentals" class="sc-e69ac761-0 dsCZKx">Fundamentals</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/f1-score-machine-learning" class="sc-e69ac761-0 dsCZKx">F1 Score in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/foundation-models" class="sc-e69ac761-0 dsCZKx">Foundation Models</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/forward-propagation" class="sc-e69ac761-0 dsCZKx">Forward Propagation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/f2-score" class="sc-e69ac761-0 dsCZKx">F2 Score</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/feature-store-for-machine-learning" class="sc-e69ac761-0 dsCZKx">Feature Store for Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/few-shot-learning" class="sc-e69ac761-0 dsCZKx">Few Shot Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/federated-learning" class="sc-e69ac761-0 dsCZKx">Federated Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/feature-learning" class="sc-e69ac761-0 dsCZKx">Feature Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/feature-selection" class="sc-e69ac761-0 dsCZKx">Feature Selection</a></li><li data-letter="G" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 fvIpti">G</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/gradient-clipping" class="sc-e69ac761-0 dsCZKx">Gradient Clipping</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/generative-adversarial-networks" class="sc-e69ac761-0 ezFGlW">Generative Adversarial Networks (GANs)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/gaussian-processes" class="sc-e69ac761-0 dsCZKx">Gaussian Processes</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/gated-recurrent-unit" class="sc-e69ac761-0 dsCZKx">Gated Recurrent Unit</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/grounding" class="sc-e69ac761-0 dsCZKx">Grounding</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/gradient-boosting-machines" class="sc-e69ac761-0 dsCZKx">Gradient Boosting Machines (GBMs)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/graphics-processing-unit-gpu" class="sc-e69ac761-0 dsCZKx">Graphics Processing Unit (GPU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/google-bard" class="sc-e69ac761-0 dsCZKx">Google's Bard</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/gradient-scaling" class="sc-e69ac761-0 dsCZKx">Gradient Scaling</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/generative-ai" class="sc-e69ac761-0 dsCZKx">Generative AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/graph-neural-networks" class="sc-e69ac761-0 dsCZKx">Graph Neural Networks</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/grapheme-to-phoneme-conversion-g2p" class="sc-e69ac761-0 dsCZKx">Grapheme-to-Phoneme Conversion (G2P)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/generative-teaching-networks" class="sc-e69ac761-0 dsCZKx">Generative Teaching Networks</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/genetic-algorithms" class="sc-e69ac761-0 dsCZKx">Genetic Algorithms in AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/ground-truth" class="sc-e69ac761-0 dsCZKx">Ground Truth in Machine Learning</a></li><li data-letter="H" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">H</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/hyperparameters" class="sc-e69ac761-0 dsCZKx">Hyperparameters</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/hidden-markov-models" class="sc-e69ac761-0 dsCZKx">Hidden Markov Models (HMMs)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/hooke-jeeves-algorithm" class="sc-e69ac761-0 dsCZKx">Hooke-Jeeves Algorithm</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/hyperparameter-tuning" class="sc-e69ac761-0 dsCZKx">Hyperparameter Tuning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/homograph-disambiguation" class="sc-e69ac761-0 dsCZKx">Homograph Disambiguation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/human-centered-ai" class="sc-e69ac761-0 dsCZKx">Human-centered AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/hidden-layer" class="sc-e69ac761-0 dsCZKx">Hidden Layer</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/human-in-the-loop-ai" class="sc-e69ac761-0 dsCZKx">Human-in-the-Loop AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/hybrid-ai" class="sc-e69ac761-0 dsCZKx">Hybrid AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/human-augmentation-with-ai" class="sc-e69ac761-0 dsCZKx">Human Augmentation with AI</a></li><li data-letter="I" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">I</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/inference-engine" class="sc-e69ac761-0 dsCZKx">Inference Engine</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/intelligent-document-processing" class="sc-e69ac761-0 dsCZKx">Intelligent Document Processing</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/imbalanced-data" class="sc-e69ac761-0 dsCZKx">Imbalanced Data</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/instruction-tuning" class="sc-e69ac761-0 dsCZKx">Instruction Tuning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/incremental-learning" class="sc-e69ac761-0 dsCZKx">Incremental Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/information-retrieval" class="sc-e69ac761-0 dsCZKx">Information Retrieval</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/image-recognition" class="sc-e69ac761-0 dsCZKx">Image Recognition</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/imagenet" class="sc-e69ac761-0 dsCZKx">ImageNet</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/inductive-bias" class="sc-e69ac761-0 dsCZKx">Inductive Bias</a></li><li data-letter="K" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">K</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/k-shingles" class="sc-e69ac761-0 dsCZKx">k-Shingles</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/keyphrase-extraction" class="sc-e69ac761-0 dsCZKx">Keyphrase Extraction</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/keras" class="sc-e69ac761-0 dsCZKx">Keras</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/knowledge-distillation" class="sc-e69ac761-0 dsCZKx">Knowledge Distillation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/knowledge-representation-and-reasoning" class="sc-e69ac761-0 dsCZKx">Knowledge Representation and Reasoning</a></li><li data-letter="L" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">L</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/llama-2" class="sc-e69ac761-0 dsCZKx">Llama 2</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/llm-collection" class="sc-e69ac761-0 dsCZKx">LLM Collection</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/latent-dirichlet-allocation" class="sc-e69ac761-0 dsCZKx">Latent Dirichlet Allocation (LDA)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/large-language-model" class="sc-e69ac761-0 dsCZKx">Large Language Model (LLM)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/loss-function" class="sc-e69ac761-0 dsCZKx">Loss Function</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/limited-memory-ai" class="sc-e69ac761-0 dsCZKx">Limited Memory AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/learning-rate" class="sc-e69ac761-0 dsCZKx">Learning Rate</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/learning-to-rank" class="sc-e69ac761-0 dsCZKx">Learning To Rank</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/logits" class="sc-e69ac761-0 dsCZKx">Logits</a></li><li data-letter="M" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">M</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/midjourney-image-generation" class="sc-e69ac761-0 dsCZKx">Midjourney (Image Generation)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/mistral" class="sc-e69ac761-0 dsCZKx">Mistral</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/mixture-of-experts" class="sc-e69ac761-0 dsCZKx">Mixture of Experts</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/multimodal-ai" class="sc-e69ac761-0 dsCZKx">Multimodal AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/multimodal-learning" class="sc-e69ac761-0 dsCZKx">Multimodal Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning" class="sc-e69ac761-0 dsCZKx">Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/multimodal-aI-models-and-modalities" class="sc-e69ac761-0 dsCZKx">Multimodal AI Models and Modalities</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/mamba" class="sc-e69ac761-0 dsCZKx">Mamba</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/models" class="sc-e69ac761-0 dsCZKx">Models</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning-bias" class="sc-e69ac761-0 dsCZKx">Machine Learning Bias</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning-life-cycle-management" class="sc-e69ac761-0 dsCZKx">Machine Learning Life Cycle Management</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-translation" class="sc-e69ac761-0 dsCZKx">Machine Translation</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/mlops" class="sc-e69ac761-0 dsCZKx">MLOps</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/monte-carlo-learning" class="sc-e69ac761-0 dsCZKx">Monte Carlo Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/multi-task-learning" class="sc-e69ac761-0 dsCZKx">Multi-task Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning-neuron" class="sc-e69ac761-0 dsCZKx">Machine Learning Neuron</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning-preprocessing" class="sc-e69ac761-0 dsCZKx">Machine Learning Preprocessing</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/model-drift" class="sc-e69ac761-0 dsCZKx">Model Drift</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/matplotlib" class="sc-e69ac761-0 dsCZKx">Matplotlib</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/Markov-decision-process" class="sc-e69ac761-0 dsCZKx">Markov Decision Process</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/model-interpretability" class="sc-e69ac761-0 dsCZKx">Model Interpretability</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/machine-learning-algorithmic-trading" class="sc-e69ac761-0 dsCZKx">Machine Learning in Algorithmic Trading</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/metacognitive-learning-models" class="sc-e69ac761-0 dsCZKx">Metacognitive Learning Models </a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/metaheuristic-algorithms" class="sc-e69ac761-0 dsCZKx">Metaheuristic Algorithms</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/multitask-prompt-tuning" class="sc-e69ac761-0 dsCZKx">Multitask Prompt Tuning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/multi-agent-systems" class="sc-e69ac761-0 dsCZKx">Multi-Agent Systems</a></li><li data-letter="N" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">N</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-processing" class="sc-e69ac761-0 dsCZKx">Natural Language Processing (NLP)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-generation" class="sc-e69ac761-0 dsCZKx">Natural Language Generation (NLG)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-understanding" class="sc-e69ac761-0 dsCZKx">Natural Language Understanding (NLU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/neuralink" class="sc-e69ac761-0 dsCZKx">Neuralink</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/naive-bayes-classifier" class="sc-e69ac761-0 dsCZKx">Naive Bayes Classifier</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/neural-radiance-fields" class="sc-e69ac761-0 dsCZKx">Neural Radiance Fields</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-querying" class="sc-e69ac761-0 dsCZKx">Natural Language Querying (NLQ)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/numpy" class="sc-e69ac761-0 dsCZKx">NumPy</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/natural-language-toolkit-nltk" class="sc-e69ac761-0 dsCZKx">Natural Language Toolkit (NLTK)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/neural-architecture-search" class="sc-e69ac761-0 dsCZKx">Neural Architecture Search</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/neural-text-to-speech-ntts" class="sc-e69ac761-0 dsCZKx">Neural Text-to-Speech (NTTS)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/named-entity-recognition" class="sc-e69ac761-0 dsCZKx">Named Entity Recognition</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/neural-style-transfer" class="sc-e69ac761-0 dsCZKx">Neural Style Transfer</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/neuroevolution" class="sc-e69ac761-0 dsCZKx">Neuroevolution</a></li><li data-letter="O" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">O</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/online-gradient-descent" class="sc-e69ac761-0 dsCZKx">Online Gradient Descent</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/overfitting-underfitting" class="sc-e69ac761-0 dsCZKx">Overfitting and Underfitting</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/openai-whisper" class="sc-e69ac761-0 dsCZKx">OpenAI Whisper</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/openai-sora" class="sc-e69ac761-0 dsCZKx">OpenAI Sora</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/out-of-distribution-detection" class="sc-e69ac761-0 dsCZKx">Out-of-Distribution Detection</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/one-shot-learning" class="sc-e69ac761-0 dsCZKx">One-Shot Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/objective-function" class="sc-e69ac761-0 dsCZKx">Objective Function</a></li><li data-letter="P" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">P</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/prompt-engineering" class="sc-e69ac761-0 dsCZKx">Prompt Engineering</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/prompt-chaining" class="sc-e69ac761-0 dsCZKx">Prompt Chaining</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/perceptron" class="sc-e69ac761-0 dsCZKx">Perceptron</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/precision-and-recall" class="sc-e69ac761-0 dsCZKx">Precision and Recall</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/packages-glossary" class="sc-e69ac761-0 dsCZKx">Packages</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/pooling-machine-learning" class="sc-e69ac761-0 dsCZKx">Pooling (Machine Learning)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/principal-component-analysis" class="sc-e69ac761-0 dsCZKx">Principal Component Analysis</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/probabilistic-models-in-machine-learning" class="sc-e69ac761-0 dsCZKx">Probabilistic Models in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/prompt-tuning" class="sc-e69ac761-0 dsCZKx">Prompt Tuning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/pytorch" class="sc-e69ac761-0 dsCZKx">PyTorch</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/pandas" class="sc-e69ac761-0 dsCZKx">Pandas</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/parametric-neural-networks%C2%A0" class="sc-e69ac761-0 dsCZKx">Parametric Neural Networks </a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/pretraining" class="sc-e69ac761-0 dsCZKx">Pretraining</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/part-of-speech-tagging" class="sc-e69ac761-0 dsCZKx">Part-of-Speech Tagging</a></li><li data-letter="Q" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">Q</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/quantum-machine-learning-algorithms" class="sc-e69ac761-0 dsCZKx">Quantum Machine Learning Algorithms</a></li><li data-letter="R" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">R</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/rule-based-ai" class="sc-e69ac761-0 dsCZKx">Rule-Based AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/roberta" class="sc-e69ac761-0 dsCZKx">RoBERTa</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/rlhf" class="sc-e69ac761-0 dsCZKx">RLHF</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/retrieval-augmented-generation" class="sc-e69ac761-0 dsCZKx">Retrieval-Augmented Generation (RAG)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/representation-learning" class="sc-e69ac761-0 dsCZKx">Representation Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/rectified-linear-unit-relu" class="sc-e69ac761-0 dsCZKx">Rectified Linear Unit (ReLU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/reproducibility-machine-learning" class="sc-e69ac761-0 dsCZKx">Reproducibility in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/restricted-boltzmann-machines" class="sc-e69ac761-0 dsCZKx">Restricted Boltzmann Machines</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/regularization" class="sc-e69ac761-0 dsCZKx">Regularization</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/recurrent-neural-networks" class="sc-e69ac761-0 dsCZKx">Recurrent Neural Networks</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/random-forest" class="sc-e69ac761-0 dsCZKx">Random Forest</a></li><li data-letter="S" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">S</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/sentiment-analysis" class="sc-e69ac761-0 dsCZKx">Sentiment Analysis</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/speech-to-text-models" class="sc-e69ac761-0 dsCZKx">Speech-to-text models</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/sequence-modeling" class="sc-e69ac761-0 dsCZKx">Sequence Modeling</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/semantic-kernel" class="sc-e69ac761-0 dsCZKx">Semantic Kernel</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/semi-supervised-learning" class="sc-e69ac761-0 dsCZKx">Semi-Supervised Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/supervised-learning" class="sc-e69ac761-0 dsCZKx">Supervised Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/support-vector-machines-svm" class="sc-e69ac761-0 dsCZKx">Support Vector Machines (SVM)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/semantic-networks" class="sc-e69ac761-0 dsCZKx">Semantic Networks</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/semi-structured-data" class="sc-e69ac761-0 dsCZKx">Semi-structured data</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/scikit-learn" class="sc-e69ac761-0 dsCZKx">Scikit-learn</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/scipy" class="sc-e69ac761-0 dsCZKx">SciPy</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/seaborn-python-package" class="sc-e69ac761-0 dsCZKx">Seaborn Python Package</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/squad" class="sc-e69ac761-0 dsCZKx">SQuAD</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/synthetic-data-for-ai-training" class="sc-e69ac761-0 dsCZKx">Synthetic Data for AI Training</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/statistical-relational-learning" class="sc-e69ac761-0 dsCZKx">Statistical Relational Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/self-healing-ai" class="sc-e69ac761-0 dsCZKx">Self-healing AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/semantic-search-algorithms" class="sc-e69ac761-0 dsCZKx">Semantic Search Algorithms</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/symbolic-ai" class="sc-e69ac761-0 dsCZKx">Symbolic AI</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/spike-neural-networks" class="sc-e69ac761-0 dsCZKx">Spike Neural Networks</a></li><li data-letter="T" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">T</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/text-to-speech-models" class="sc-e69ac761-0 dsCZKx">Text-to-Speech Models</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/transformers" class="sc-e69ac761-0 dsCZKx">Transformers</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/tensor-processing-unit-tpu" class="sc-e69ac761-0 dsCZKx">Tensor Processing Unit (TPU)</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/tokenization" class="sc-e69ac761-0 dsCZKx">Tokenization</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/techniques" class="sc-e69ac761-0 dsCZKx">Techniques</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/topic-modeling" class="sc-e69ac761-0 dsCZKx">Topic Modeling</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/transfer-learning" class="sc-e69ac761-0 dsCZKx">Transfer Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/tensorflow" class="sc-e69ac761-0 dsCZKx">TensorFlow</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/the-pile" class="sc-e69ac761-0 dsCZKx">The Pile</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/test-data-set" class="sc-e69ac761-0 dsCZKx">Test Data Set</a></li><li data-letter="U" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">U</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/uncertainty-machine-learning" class="sc-e69ac761-0 dsCZKx">Uncertainty in Machine Learning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/unsupervised-learning" class="sc-e69ac761-0 dsCZKx">Unsupervised Learning</a></li><li data-letter="V" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">V</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/vanishing-and-exploding-gradients" class="sc-e69ac761-0 dsCZKx">Vanishing and Exploding Gradients</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/voice-cloning" class="sc-e69ac761-0 dsCZKx">Voice Cloning</a><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/validation-data-set" class="sc-e69ac761-0 dsCZKx">Validation Data Set</a></li><li data-letter="W" class="sc-ace17a57-0 eTCxuy"><div width="100%" class="sc-6293d692-0 jWSoNY"><span font-size="textLg" font-weight="medium" class="sc-b323b31-0 jgUCVJ">W</span></div><a rel="" target="" font-size="textLg" font-weight="medium" href="/ai-glossary/winnow-algorithm" class="sc-e69ac761-0 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Deepgram</span></div></div></div></footer></div></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"slug":["generative-adversarial-networks"],"pageData":{"templateGlossary":{"__typename":"TemplateGlossaryRecord","id":"XIY2ST27QQKF3YEBXH0_dQ","seo":null,"slug":"generative-adversarial-networks","title":"Generative Adversarial Networks (GANs)","categoryPage":false,"excerpt":{"__typename":"TemplateGlossaryModelExcerptField","value":{"schema":"dast","document":{"type":"root","children":[{"type":"paragraph","children":[{"type":"span","value":"Generative Adversarial Networks (GANs) are generative models developed by combining two neural network architectures: the generator and the discriminator. In this setup, both networks compete as “adversaries.” The generator's primary role is to create new data samples, and the discriminator's job is to assess their authenticity by distinguishing them from actual samples."}]}]}}},"body":{"__typename":"TemplateGlossaryModelBodyField","blocks":[],"links":[{"__typename":"ComponentImageRecord","id":"Z9Xp_t5MQrqzWfW1TE7PWA","caption":"","imageDesktop":{"__typename":"FileField","id":"E_pEufmcT9uE5-ZrwOz3Gg","title":null,"filename":"image2.png","size":3128575,"alt":null,"url":"https://www.datocms-assets.com/96965/1701096542-image2.png","width":1920,"height":1238},"imageMobile":null,"link":""},{"__typename":"ComponentImageRecord","id":"UpBk_vE8TrGAMIQS1njkFg","caption":"","imageDesktop":{"__typename":"FileField","id":"B84jekGJTCWvAmWh_hKHdg","title":null,"filename":"image1.png","size":338663,"alt":null,"url":"https://www.datocms-assets.com/96965/1701096525-image1.png","width":1920,"height":1088},"imageMobile":null,"link":""},{"__typename":"ComponentImageRecord","id":"LQsb3r78Q9OLfiVYNjfV-A","caption":"","imageDesktop":{"__typename":"FileField","id":"HniNryQ6RWmJwHA-PLWV1w","title":null,"filename":"image3-modified.png","size":109544,"alt":null,"url":"https://www.datocms-assets.com/96965/1703085557-image3-modified.png","width":1999,"height":158},"imageMobile":null,"link":""},{"__typename":"ComponentImageRecord","id":"E4DkGWonT4mSPsfHtAF5ZQ","caption":"","imageDesktop":{"__typename":"FileField","id":"BJlILSYrQ8W3Jjos1AsV3Q","title":null,"filename":"image8-modified.png","size":119288,"alt":null,"url":"https://www.datocms-assets.com/96965/1703085732-image8-modified.png","width":1999,"height":155},"imageMobile":null,"link":""},{"__typename":"ComponentImageRecord","id":"O66IgbhlTgSmSLvq_3kcOg","caption":"","imageDesktop":{"__typename":"FileField","id":"TOkPdp_DRKKkdvlOkJwxyQ","title":null,"filename":"image6-modified.png","size":107374,"alt":null,"url":"https://www.datocms-assets.com/96965/1703085771-image6-modified.png","width":1999,"height":142},"imageMobile":null,"link":""},{"__typename":"ComponentImageRecord","id":"AGJOfobER-azW9kVyt5ZiA","caption":"Data is everything in the world of AI. But some data is better than others. [This article](https://deepgram.com/learn/the-ai-echo-chamber-model-collapse-synthetic-data-risks) unveils the unspoken truth of synthetic data.","imageDesktop":{"__typename":"FileField","id":"QU2pq6FHQ4iSD6II6lQvXA","title":null,"filename":"screen-shot-2024-06-18-at-11-48-34-am.png","size":3258631,"alt":null,"url":"https://www.datocms-assets.com/96965/1718736518-screen-shot-2024-06-18-at-11-48-34-am.png","width":1796,"height":1088},"imageMobile":null,"link":""},{"__typename":"ComponentImageRecord","id":"OcUPVLYOTdiOH90vXDPFbA","caption":"Mixture of Experts (MoE) is a method that presents an efficient approach to dramatically increasing a model’s capabilities without introducing a proportional amount of computational overhead. To learn more, check out [this guide](https://deepgram.com/learn/mixture-of-experts-ml-model-guide)!","imageDesktop":{"__typename":"FileField","id":"cnmz5FWiQv2S--QFhqNL-g","title":null,"filename":"screen-shot-2024-06-16-at-2-06-22-am.png","size":2233720,"alt":null,"url":"https://www.datocms-assets.com/96965/1718528786-screen-shot-2024-06-16-at-2-06-22-am.png","width":1512,"height":788},"imageMobile":null,"link":""}],"value":{"schema":"dast","document":{"type":"root","children":[{"type":"heading","level":2,"children":[{"type":"span","value":"Introduction to Generative Adversarial Neural Networks (GANs)"}]},{"type":"paragraph","children":[{"type":"span","value":"Generative Adversarial Networks (GANs) are generative models developed by combining two neural network architectures: the generator and the discriminator. In this setup, both networks compete as “adversaries.” The generator's primary role is to create new data samples, and the discriminator's job is to assess their authenticity by distinguishing them from actual samples."}]},{"type":"paragraph","children":[{"type":"span","value":"GANs typically use unsupervised learning techniques to learn from the data distribution without needing predefined labels. Through the adversarial relationship (competition) between the generator and discriminator, the model progressively improves itself to produce outputs that closely align with the expected distribution, creating new, realistic samples that are hard to tell from real samples."}]},{"type":"paragraph","children":[{"type":"span","value":"Their ability to generate high-quality, realistic data has made GANs popular across various domains. They are widely used in artistic applications like style transfer and synthetic data creation."}]},{"type":"paragraph","children":[{"item":"Z9Xp_t5MQrqzWfW1TE7PWA","type":"inlineItem"}]},{"type":"heading","level":2,"children":[{"type":"span","value":"History of GANs"}]},{"type":"paragraph","children":[{"type":"span","value":"The journey of GANs began as a solution to the need for high-dimensional, realistic, and diverse data samples. Traditional machine learning approaches often struggled with handling complex and varied real-world data, prompting GANs to emerge as a solution that expanded the possibilities of generative models. This overview offers a concise history, highlighting various endeavors by key contributors in the evolution of GANs."}]},{"type":"heading","level":3,"children":[{"type":"span","value":"2014: Origin of GANs"}]},{"type":"paragraph","children":[{"type":"span","value":"Goodfellow et al. (2014) "},{"url":"https://arxiv.org/abs/1406.2661","type":"link","children":[{"type":"span","marks":["underline"],"value":"proposed the GAN framework"}]},{"type":"span","value":", introducing the generator (G) and discriminator (D) models to estimate generative models without relying on "},{"url":"https://en.wikipedia.org/wiki/Markov_chain","type":"link","children":[{"type":"span","marks":["underline"],"value":"Markov chains"}]},{"type":"span","value":" or unrolled "},{"url":"https://en.wikipedia.org/wiki/Approximate_inference","type":"link","children":[{"type":"span","marks":["underline"],"value":"approximate inference"}]},{"type":"span","value":" networks, which were common in generative models of the time."}]},{"type":"paragraph","children":[{"type":"span","value":"GANs gained attention for their ability to generate realistic images, sparking increased research interest in this area."}]},{"type":"heading","level":3,"children":[{"type":"span","value":"2015: Introduction of DCGAN"}]},{"type":"paragraph","children":[{"type":"span","value":"Radford et al. (2015) introduced "},{"url":"https://arxiv.org/abs/1511.06434","type":"link","children":[{"type":"span","marks":["underline"],"value":"Deep Convolutional Generative Adversarial Networks (DCGAN)"}]},{"type":"span","value":", which set new "},{"url":"https://deepgram.com/ai-glossary/benchmarking","type":"link","children":[{"type":"span","value":"benchmarks"}]},{"type":"span","value":" for GAN architecture, focusing on generating realistic images through deep convolutional networks. This innovation improved stability and performance in image generation tasks. It introduced innovative concepts like "},{"url":"https://machinelearningmastery.com/how-to-interpolate-and-perform-vector-arithmetic-with-faces-using-a-generative-adversarial-network/","type":"link","children":[{"type":"span","marks":["underline"],"value":"image arithmetic"}]},{"type":"span","value":", which enabled extracting features from specific latent spaces by conditioning images on others."}]},{"type":"paragraph","children":[{"type":"span","value":"However, DCGAN faced limitations in the image resolution of the generated output, which led to the development of "},{"url":"https://arxiv.org/abs/1809.11096","type":"link","children":[{"type":"span","marks":["underline"],"value":"BigGANs"}]},{"type":"span","value":" by Brock et al. (2018). This solution generates high-resolution and realistic images. BigGAN is recognized as one of the largest and most computationally intensive GAN models to date."}]},{"type":"heading","level":3,"children":[{"type":"span","value":"2017: Progressive GAN"}]},{"type":"paragraph","children":[{"type":"span","value":"From NVIDIA, Karras et al. (2017) developed "},{"url":"https://arxiv.org/abs/1710.10196","type":"link","children":[{"type":"span","marks":["underline"],"value":"Progressive GAN"}]},{"type":"span","value":", which scaled up images progressively, starting from smaller resolutions (like 4 x 4 or 16 x 16) and gradually increasing to larger sizes. This architecture improved the stability and detail of the image generated."}]},{"type":"heading","level":3,"children":[{"type":"span","value":"2018: Style GAN 1"}]},{"type":"paragraph","children":[{"type":"span","value":"In "},{"url":"https://arxiv.org/abs/1812.04948","type":"link","children":[{"type":"span","marks":["underline"],"value":"the Style GAN 1 model"}]},{"type":"span","value":", each generator is conceptualized as a distinct style, with each style influencing effects at specific scales, such as coarse (overall structure or layout), middle (facial expressions or patterns), and delicate (lightning and shading or shape of nose) styles. "}]},{"type":"paragraph","children":[{"type":"span","value":"The researchers achieved that by mapping images from the latent space, ‘z’, to the intermediate latent space, ‘w’, through a dedicated mapping network."}]},{"type":"heading","level":3,"children":[{"type":"span","value":"2019: Style GAN 2"}]},{"type":"paragraph","children":[{"url":"https://arxiv.org/abs/1912.04958","type":"link","children":[{"type":"span","marks":["underline"],"value":"StyleGAN 2"}]},{"type":"span","value":" improved upon its predecessor by addressing artifacts like phase and water droplet-like artifacts to enhance image quality and realism. It became a state-of-the-art model in GANs compared to its predecessors."}]},{"type":"paragraph","children":[{"type":"span","value":"There has been a great deal of improvement in the GAN model since 2014. Despite its transformative impact on generative modeling and image synthesis, ethical concerns have emerged due to issues like deepfake technology and intellectual property infringement, such as the notable case of "},{"url":"https://www.itsnicethat.com/features/ones-to-watch-2020-robbie-barrat-digital-240220","type":"link","children":[{"type":"span","marks":["underline"],"value":"Robbie Barrat using GANs for AI art"}]},{"type":"span","value":". "}]},{"type":"paragraph","children":[{"type":"span","value":"These concerns continue, but the GAN model continues to play a crucial role within the broader field of artificial intelligence and remains an important generative technology."}]},{"type":"heading","level":2,"children":[{"type":"span","value":"The Architecture of GANs"}]},{"type":"paragraph","children":[{"type":"span","value":"At the heart of the GAN framework is adversarial learning, which involves the generator and the discriminator networks that work with and against each other as part of the architecture. "}]},{"type":"paragraph","children":[{"type":"span","value":"This setup is a "},{"url":"https://en.wikipedia.org/wiki/Zero-sum_game","type":"link","children":[{"type":"span","marks":["underline"],"value":"zero-sum game"}]},{"type":"span","value":" in "},{"url":"https://en.wikipedia.org/wiki/Game_theory","type":"link","children":[{"type":"span","marks":["underline"],"value":"game theory"}]},{"type":"span","value":", where the success of one participant comes at the expense of the other. The networks work together by fitting the training data and learning from the data by adjusting the weights based on the errors to minimize the loss. Each network trains to work against the other. The goal is to reach a state of equilibrium where the discriminator cannot reliably distinguish between real and fake samples."}]},{"type":"paragraph","children":[{"type":"span","value":"The generator initiates the process in GANs by producing artificial data from a random noise source. This data undergoes repeated improvements as the generator enhances its ability to mimic natural data distribution, resulting in outputs that progressively look more like actual data. Simultaneously, the discriminator assesses how genuine the generator's output appears, progressively learning to differentiate between real and generated data. "}]},{"type":"paragraph","children":[{"type":"span","value":"As the discriminator improves at identifying generated data, it pushes the generator to create increasingly realistic data. As the training process continues, at some point, the discriminator finds it challenging to distinguish between generated and real data."}]},{"type":"paragraph","children":[{"item":"UpBk_vE8TrGAMIQS1njkFg","type":"inlineItem"}]},{"type":"heading","level":4,"children":[{"type":"span","value":"The role of the generator"}]},{"type":"paragraph","children":[{"type":"span","value":"The generator initializes by generating synthetic data from random noise. This initial data is often far from the desired output but serves as a starting point for training the network. The generator then observes the real data distribution and adjusts its outputs accordingly. This objective is to generate data that closely resembles the real data so that the discriminator cannot tell the difference. The training process (adjusting the weights to minimize the error) is progressive, with the generator learning from each interaction with the discriminator."}]},{"type":"heading","level":4,"children":[{"type":"span","value":"The role of the discriminator"}]},{"type":"paragraph","children":[{"type":"span","value":"The role of the discriminator in the GAN architecture is to evaluate the authenticity of the data produced by the generator. It does this by distinguishing between actual data and the synthetic data created by the generator."}]},{"type":"paragraph","children":[{"type":"span","value":"The discriminator fits the training data and learns to discern the differences between real and synthetic data from the generator. The objective of the discriminator is to assign high probabilities to real data points and low probabilities to synthetic points. Over time, the discriminator's ability to distinguish between real and synthetic data becomes more refined, providing a moving target for the generator to improve against."}]},{"type":"paragraph","children":[{"type":"span","value":"In this context, it estimates the probability "},{"type":"span","marks":["code"],"value":"P(Y | X = x) "},{"type":"span","value":"that a given point is real, adjusting probabilities to assign a value of 1 for real examples and 0 for fake data points."}]},{"type":"heading","level":2,"children":[{"type":"span","marks":["strong"],"value":"How GANs Learn: Training and Backpropagation"}]},{"type":"paragraph","children":[{"type":"span","value":"This learning process for GANs involves adversarial optimization of both the generator and discriminator. In other words, the objective of this process is to find a global optimum for the loss functions of both networks. This optimization achieves a state where the generator effectively reproduces the real data distribution. "}]},{"type":"paragraph","children":[{"type":"span","value":"The generator seeks to minimize its loss function, which is typically a measure of how easily the discriminator can identify its outputs as fake. Conversely, the discriminator aims to maximize its loss function, reflecting its ability to identify real and generated data correctly."}]},{"type":"heading","level":3,"children":[{"type":"span","value":"The generator's loss function"}]},{"type":"paragraph","children":[{"type":"span","value":"Here is the mathematical representation of the generator’s loss function:"}]},{"type":"paragraph","children":[{"item":"LQsb3r78Q9OLfiVYNjfV-A","type":"inlineItem"}]},{"type":"paragraph","children":[{"type":"span","marks":["code"],"value":"E"},{"type":"span","value":" denotes the "},{"url":"https://en.wikipedia.org/wiki/Expected_value#:~:text=In%20probability%20theory%2C%20the%20expected,generalization%20of%20the%20weighted%20average.","type":"link","children":[{"type":"span","marks":["underline"],"value":"expectation"}]},{"type":"span","value":" over the latent space (z) and the real data distribution (x)."}]},{"type":"paragraph","children":[{"type":"span","value":"The goal is to "},{"type":"span","marks":["emphasis"],"value":"minimize"},{"type":"span","value":" the combination of two log-probability terms:"}]},{"type":"list","style":"bulleted","children":[{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["code"],"value":"log(D(G(z))"},{"type":"span","value":" represents the log probability that the discriminator (D) classifies the generated data as real."}]}]},{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["code"],"value":"log(1 - D(x))"},{"type":"span","value":" represents the log probability that the discriminator correctly identifies real data as real."}]}]}]},{"type":"heading","level":4,"children":[{"type":"span","value":"The discriminator's loss function"}]},{"type":"paragraph","children":[{"type":"span","value":"Here is the mathematical representation of the discriminator’s loss function:"}]},{"type":"paragraph","children":[{"item":"E4DkGWonT4mSPsfHtAF5ZQ","type":"inlineItem"}]},{"type":"paragraph","children":[{"type":"span","value":"The function consists of two terms: "}]},{"type":"list","style":"bulleted","children":[{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","marks":["code"],"value":"log(D(G(z)) "},{"type":"span","value":"which measures the probability the discriminator assigns to the generated data, aiming to "},{"type":"span","marks":["emphasis"],"value":"maximize"},{"type":"span","value":" it."}]}]},{"type":"listItem","children":[{"type":"paragraph","children":[{"type":"span","value":"The second term, "},{"type":"span","marks":["code"],"value":"log(1 - D(x))"},{"type":"span","value":" evaluates the probability assigned by the discriminator to real data, striving to "},{"type":"span","marks":["emphasis"],"value":"minimize"},{"type":"span","value":" it."}]}]}]},{"type":"paragraph","children":[{"type":"span","value":"The overall aim is to find the optimal parameters for the generator that make the generated data indistinguishable from real data."}]},{"type":"heading","level":4,"children":[{"type":"span","value":"Unified training objective"}]},{"type":"paragraph","children":[{"item":"O66IgbhlTgSmSLvq_3kcOg","type":"inlineItem"}]},{"type":"paragraph","children":[{"type":"span","value":"Combining the loss functions of each network, the unified training objective formula shows the essence of the entire training process. It emphasizes minimizing the generator's loss and maximizing the discriminator's loss, which brings both networks to an optimum point."}]},{"type":"heading","level":3,"children":[{"type":"span","value":"Backpropagation in GANs"}]},{"type":"paragraph","children":[{"type":"span","value":"Backpropagation in the network calculates the gradients of the loss functions, enabling the model to adjust its parameters and progressively improve performance. Typically, it involves adjusting errors backwards through the network, which helps refine both the generator and discriminator networks by minimizing their losses."}]},{"type":"heading","level":3,"children":[{"type":"span","value":"Evaluating GAN Performance"}]},{"type":"paragraph","children":[{"type":"span","value":"Evaluating the performance of GANs is more complex than assessing traditional machine learning models. You assess generative models using a combination of metrics that address different aspects of the generated output. Some metrics, like the "},{"url":"https://arxiv.org/pdf/1801.01973.pdf#:~:text=The%20Inception%20Score%20is%20a,from%20the%20CIFAR%2D10%20dataset.","type":"link","children":[{"type":"span","marks":["underline"],"value":"Inception Score (IS)"}]},{"type":"span","value":", "},{"url":"https://en.wikipedia.org/wiki/Fr%C3%A9chet_inception_distance","type":"link","children":[{"type":"span","marks":["underline"],"value":"Frechet Inception Distance (FID)"}]},{"type":"span","value":", Human Evaluation, "},{"url":"https://en.wikipedia.org/wiki/Precision_and_recall","type":"link","children":[{"type":"span","marks":["underline"],"value":"Precision, Recall"}]},{"type":"span","value":", and "},{"url":"https://en.wikipedia.org/wiki/F-score","type":"link","children":[{"type":"span","marks":["underline"],"value":"F1-score"}]},{"type":"span","value":", are combined to evaluate GAN performance based on the task it is used for."}]},{"type":"paragraph","children":[{"type":"span","value":"Generally, these metrics evaluate different aspects of performance, such as image quality, diversity, similarity to real data distribution, and the ability to deceive a discriminator. The ultimate goal is to ensure that the generator produces high-quality, diverse, and realistic samples."}]},{"type":"heading","level":2,"children":[{"type":"span","marks":["strong"],"value":"GANs in Action: Applications in Computer Vision"}]},{"type":"paragraph","children":[{"type":"span","value":"GANs come to life in computer vision with transformative applications ranging from image synthesis to object detection. Here are a few applications of GANs."}]},{"type":"heading","level":3,"children":[{"type":"span","value":"Image generation and enhancement"}]},{"type":"paragraph","children":[{"type":"span","value":"Models like StyleGAN, BigGAN, and other conditioned GANs have demonstrated great proficiency in generating realistic, high-resolution images or sketches. Their ability to capture fine details in images and produce diverse outputs makes them useful for various tasks, from artistic image creation to generating synthetic datasets for training ML models."}]},{"type":"heading","level":3,"children":[{"type":"span","value":"Data augmentation for machine learning"}]},{"type":"paragraph","children":[{"url":"https://en.wikipedia.org/wiki/Data_augmentation","type":"link","children":[{"type":"span","marks":["underline"],"value":"Data augmentation"}]},{"type":"span","value":" improves model performance in computer vision tasks and ML by diversifying the training dataset. When realistic variations of existing datasets are created with GANs for a computer vision task, it can enhance the robustness and generalization strength of the model, reducing the chances of overfitting. This is particularly valuable in fields where data is limited and challenging to collect."}]},{"type":"paragraph","children":[{"item":"AGJOfobER-azW9kVyt5ZiA","type":"inlineItem"}]},{"type":"heading","level":3,"children":[{"type":"span","value":"Real-world examples and case studies"}]},{"type":"paragraph","children":[{"url":"https://arxiv.org/abs/1703.10593","type":"link","children":[{"type":"span","marks":["underline"],"value":"CycleGAN"}]},{"type":"span","value":" facilitates the "},{"url":"https://arxiv.org/pdf/1903.03374.pdf","type":"link","children":[{"type":"span","marks":["underline"],"value":"translation of images in medical imaging"}]},{"type":"span","value":" across different modalities, aiding in diagnostic tasks or "},{"url":"https://ieeexplore.ieee.org/document/8553236","type":"link","children":[{"type":"span","marks":["underline"],"value":"voice conversion"}]},{"type":"span","value":". GANs are also used for "},{"url":"https://arxiv.org/abs/1805.07112","type":"link","children":[{"type":"span","marks":["underline"],"value":"image captioning"}]},{"type":"span","value":" or video synthesis, allowing the generation of descriptive captions for images and the creation of lifelike video sequences. These technologies have found significant use in virtual environments and simulations, among other applications."}]},{"type":"heading","level":2,"children":[{"type":"span","marks":["strong"],"value":"Overcoming the Challenges in GANs"}]},{"type":"paragraph","children":[{"type":"span","value":"Despite their numerous applications and advancements, GANs face several challenges."}]},{"type":"heading","level":3,"children":[{"type":"span","value":"Stability and convergence issues"}]},{"type":"paragraph","children":[{"type":"span","value":"When training GANs, stability and convergence issues (the adversarial networks have difficulties reaching a stable and desired state) are usually a concern. Techniques like "},{"url":"https://arxiv.org/abs/1802.05957","type":"link","children":[{"type":"span","marks":["underline"],"value":"spectral normalization"}]},{"type":"span","value":" and "},{"url":"https://arxiv.org/abs/1710.10196","type":"link","children":[{"type":"span","marks":["underline"],"value":"progressive growing"}]},{"type":"span","value":" have stabilised the training process and addressed these issues. Different optimization techniques (like utilizing different learning rates for the generator and discriminator) can contribute to more stable and reliable training."}]},{"type":"heading","level":3,"children":[{"type":"span","value":"Mode collapse and diversity"}]},{"type":"paragraph","children":[{"url":"https://en.wikipedia.org/wiki/Generative_adversarial_network","type":"link","children":[{"type":"span","marks":["underline"],"value":"Mode collapse"}]},{"type":"span","value":" occurs when a GAN fails to capture the full variability of the training data, leading to repetitive or limited outputs. Addressing this challenge involves incorporating "},{"url":"https://paperswithcode.com/method/minibatch-discrimination","type":"link","children":[{"type":"span","marks":["underline"],"value":"minibatch discrimination"}]},{"type":"span","value":" or "},{"url":"https://arxiv.org/abs/1910.12027","type":"link","children":[{"type":"span","marks":["underline"],"value":"consistency regularization"}]},{"type":"span","value":" to make the generated samples more consistent. Using progressive growing and conditional GANs has proven helpful in ensuring the generated samples are varied and look real. It helps avoid generator mode collapse."}]},{"type":"heading","level":3,"children":[{"type":"span","value":"Ethical considerations and misuse prevention"}]},{"type":"paragraph","children":[{"type":"span","value":"Concerns about GAN technology, especially in making "},{"url":"https://en.wikipedia.org/wiki/Deepfake","type":"link","children":[{"type":"span","marks":["underline"],"value":"Deepfakes"}]},{"type":"span","value":", have been seen within the research community and mainstream media. Efforts are underway to develop better methods for detecting Deepfakes and making AI more transparent. "},{"url":"https://en.wikipedia.org/wiki/Ethics_of_artificial_intelligence","type":"link","children":[{"type":"span","marks":["underline"],"value":"Responsible AI"}]},{"type":"span","value":" efforts and ethical guidelines are committed to reducing the potential harms of GAN misuse, like spreading misinformation and privacy breaches."}]},{"type":"heading","level":2,"children":[{"type":"span","marks":["strong"],"value":"The Future of GANs: Beyond Image Generation"}]},{"type":"paragraph","children":[{"type":"span","value":"GANs have come a long way with lots of improvements, giving us different versions like Wasserstein GAN, which helps with stability and reduces problems like mode collapse; conditional GAN, which is task-specific and CycleGAN (great for speech or audio conversions); "},{"url":"https://paperswithcode.com/method/specgan","type":"link","children":[{"type":"span","marks":["underline"],"value":"SpecGAN"}]},{"type":"span","value":", "},{"url":"https://arxiv.org/abs/1802.04208","type":"link","children":[{"type":"span","marks":["underline"],"value":"WaveGAN"}]},{"type":"span","value":", and "},{"url":"https://arxiv.org/abs/1902.08710","type":"link","children":[{"type":"span","marks":["underline"],"value":"GANsynth"}]},{"type":"span","value":", which can be used to generate spectrographs and audio, among other innovations. Researchers are also exploring how GANs can work with new "},{"url":"https://deepgram.com/ai-glossary/deep-learning","type":"link","children":[{"type":"span","value":"deep learning"}]},{"type":"span","value":" tools like "},{"url":"https://deepgram.com/ai-glossary/transformers","type":"link","children":[{"type":"span","marks":["underline"],"value":"Transformers"}]},{"type":"span","value":", "},{"url":"https://arxiv.org/abs/1811.02033","type":"link","children":[{"type":"span","marks":["underline"],"value":"Physics-Informed Neural Networks"}]},{"type":"span","value":", "},{"url":"https://deepgram.com/ai-glossary/large-language-model","type":"link","children":[{"type":"span","marks":["underline"],"value":"Large Language models"}]},{"type":"span","value":", and "},{"url":"https://arxiv.org/abs/2206.02262","type":"link","children":[{"type":"span","marks":["underline"],"value":"Diffusion models"}]},{"type":"span","value":".\n\nGANs have moved beyond their initial image-related focus and are now used in tasks like text-to-image synthesis, image-to-image translation, "},{"url":"https://arxiv.org/abs/2107.11098","type":"link","children":[{"type":"span","marks":["underline"],"value":"time series"}]},{"type":"span","value":", and "},{"url":"https://arxiv.org/ftp/arxiv/papers/2104/2104.09917.pdf","type":"link","children":[{"type":"span","marks":["underline"],"value":"semantic segmentation"}]},{"type":"span","value":". These and many more advancements exemplify the ever-evolving research on GANs. 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