[2203.00667] Generative Adversarial Networks

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Arguably, their most substantial impact has been in the area of computer vision, where they achieve state-of-the-art image generation. This chapter gives an introduction to GANs, by discussing their principle mechanism and presenting some of their inherent problems during training and evaluation. We focus on these three issues: (1) mode collapse, (2) vanishing gradients, and (3) generation of low-quality images. We then list some architecture-variant and loss-variant GANs that remedy the above challenges. Lastly, we present two utilization examples of GANs for real-world applications: Data augmentation and face images generation."/> <meta name="twitter:site" content="@arxiv"/> <meta name="twitter:card" content="summary"/> <meta name="twitter:title" content="Generative Adversarial Networks"/> <meta name="twitter:description" content="Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data, and are immensely used in both the academia and industry in many domains. Arguably, their most..."/> <meta name="twitter:image" content="https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png"/> <meta name="twitter:image:alt" content="arXiv logo"/> <link rel="stylesheet" media="screen" type="text/css" href="/static/browse/0.3.4/css/tooltip.css"/><link rel="stylesheet" media="screen" type="text/css" href="https://static.arxiv.org/js/bibex-dev/bibex.css?20200709"/> <script src="/static/browse/0.3.4/js/mathjaxToggle.min.js" type="text/javascript"></script> <script src="//code.jquery.com/jquery-latest.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/js-cookie@2/src/js.cookie.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/dompurify@2.3.5/dist/purify.min.js"></script> <script src="/static/browse/0.3.4/js/toggle-labs.js?20241022" type="text/javascript"></script> <script src="/static/browse/0.3.4/js/cite.js" type="text/javascript"></script><meta name="citation_title" content="Generative Adversarial Networks" /><meta name="citation_author" content="Cohen, Gilad" /><meta name="citation_author" content="Giryes, Raja" /><meta name="citation_date" content="2022/03/01" /><meta name="citation_online_date" content="2022/03/01" /><meta name="citation_pdf_url" content="http://arxiv.org/pdf/2203.00667" /><meta name="citation_arxiv_id" content="2203.00667" /><meta name="citation_abstract" content="Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data, and are immensely used in both the academia and industry in many domains. Arguably, their most substantial impact has been in the area of computer vision, where they achieve state-of-the-art image generation. This chapter gives an introduction to GANs, by discussing their principle mechanism and presenting some of their inherent problems during training and evaluation. We focus on these three issues: (1) mode collapse, (2) vanishing gradients, and (3) generation of low-quality images. We then list some architecture-variant and loss-variant GANs that remedy the above challenges. Lastly, we present two utilization examples of GANs for real-world applications: Data augmentation and face images generation." /> </head> <body class="with-cu-identity"> <aside class="slider-wrapper bps-banner forum blue"> <a class="close-slider bps-banner" href="#"><img src="/static/browse/0.3.4/images/icons/close-slider.png" alt="close this message"></a> <div class="columns"> <img role="presentation" class="bps-banner-image" src="/static/browse/0.3.4/images/icons/smileybones-pixel.png" alt="arXiv Smilely Bones"> <div class="copy-donation bps-banner"> <h2>Happy Giving Tuesday - support arXiv today!</h2> <p>Thank you to everyone who makes arXiv possible. 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Adversarial Networks</h1> <div class="authors"><span class="descriptor">Authors:</span><a href="https://arxiv.org/search/cs?searchtype=author&query=Cohen,+G" rel="nofollow">Gilad Cohen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Giryes,+R" rel="nofollow">Raja Giryes</a></div> <div id="download-button-info" hidden>View a PDF of the paper titled Generative Adversarial Networks, by Gilad Cohen and Raja Giryes</div> <a class="mobile-submission-download" href="/pdf/2203.00667">View PDF</a> <blockquote class="abstract mathjax"> <span class="descriptor">Abstract:</span>Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data, and are immensely used in both the academia and industry in many domains. Arguably, their most substantial impact has been in the area of computer vision, where they achieve state-of-the-art image generation. This chapter gives an introduction to GANs, by discussing their principle mechanism and presenting some of their inherent problems during training and evaluation. We focus on these three issues: (1) mode collapse, (2) vanishing gradients, and (3) generation of low-quality images. We then list some architecture-variant and loss-variant GANs that remedy the above challenges. Lastly, we present two utilization examples of GANs for real-world applications: Data augmentation and face images generation. </blockquote>  <div class="metatable"> <table summary="Additional metadata"><tr> <td class="tablecell label">Subjects:</td> <td class="tablecell subjects"> <span class="primary-subject">Computer Vision and Pattern Recognition (cs.CV)</span>; Machine Learning (cs.LG)</td> </tr><tr> <td class="tablecell label">Cite as:</td> <td class="tablecell arxivid"><span class="arxivid"><a href="https://arxiv.org/abs/2203.00667">arXiv:2203.00667</a> [cs.CV]</span></td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxividv">(or <span class="arxivid"> <a href="https://arxiv.org/abs/2203.00667v1">arXiv:2203.00667v1</a> [cs.CV]</span> for this version) </td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxivdoi"> <a href="https://doi.org/10.48550/arXiv.2203.00667" id="arxiv-doi-link">https://doi.org/10.48550/arXiv.2203.00667</a><div 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[2203.00667] Generative Adversarial Networks