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class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Inception_v4"> <div class="vector-toc-text"> <span class="vector-toc-numb">1.4</span> <span>Inception v4</span> </div> </a> <ul id="toc-Inception_v4-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-Xception" class="vector-toc-list-item vector-toc-level-2"> <a class="vector-toc-link" href="#Xception"> <div class="vector-toc-text"> <span class="vector-toc-numb">1.5</span> <span>Xception</span> </div> </a> <ul id="toc-Xception-sublist" class="vector-toc-list"> </ul> </li> </ul> </li> <li id="toc-References" class="vector-toc-list-item vector-toc-level-1 vector-toc-list-item-expanded"> <a class="vector-toc-link" href="#References"> <div class="vector-toc-text"> <span class="vector-toc-numb">2</span> <span>References</span> </div> </a> <ul id="toc-References-sublist" class="vector-toc-list"> </ul> </li> <li id="toc-External_links" class="vector-toc-list-item vector-toc-level-1 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.mw-parser-output .infobox-table th,body.skin--responsive .mw-parser-output .infobox-table td{padding-left:inherit;padding-right:inherit}}</style><table class="infobox vevent"><caption class="infobox-title summary">Inception</caption><tbody><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="/wiki/Programmer" title="Programmer">Original author(s)</a></th><td class="infobox-data"><a href="/wiki/Google_AI" title="Google AI">Google AI</a></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;">Initial release</th><td class="infobox-data">2014</td></tr><tr style="display: none;"><td colspan="2" class="infobox-full-data"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1257001546" /></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="/wiki/Software_release_life_cycle" title="Software release life cycle">Stable release</a></th><td class="infobox-data"><div style="margin:0px;">v4 / 2017</div></td></tr><tr style="display:none"><td colspan="2"> </td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="/wiki/Repository_(version_control)" title="Repository (version control)">Repository</a></th><td class="infobox-data"><span class="url"><a rel="nofollow" class="external text" href="https://github.com/tensorflow/models/blob/master/research/slim/README.md">github<wbr />.com<wbr />/tensorflow<wbr />/models<wbr />/blob<wbr />/master<wbr />/research<wbr />/slim<wbr />/README<wbr />.md</a></span></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="/wiki/Software_categories#Categorization_approaches" title="Software categories">Type</a></th><td class="infobox-data"><style data-mw-deduplicate="TemplateStyles:r1126788409">.mw-parser-output .plainlist ol,.mw-parser-output .plainlist ul{line-height:inherit;list-style:none;margin:0;padding:0}.mw-parser-output .plainlist ol li,.mw-parser-output .plainlist ul li{margin-bottom:0}</style><div class="plainlist" style="margin-left:1em;text-indent:-1em;"> <ul><li><a href="/wiki/Convolutional_neural_network" title="Convolutional neural network">Convolutional neural network</a></li></ul> </div></td></tr><tr><th scope="row" class="infobox-label" style="white-space: nowrap;"><a href="/wiki/Software_license" title="Software license">License</a></th><td class="infobox-data"><a href="/wiki/Apache_2.0" class="mw-redirect" title="Apache 2.0">Apache 2.0</a></td></tr></tbody></table> <p><b>Inception</b><sup id="cite_ref-szegedy_1-0" class="reference"><a href="#cite_note-szegedy-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> is a family of <a href="/wiki/Convolutional_neural_network" title="Convolutional neural network">convolutional neural network</a> (CNN) for <a href="/wiki/Computer_vision" title="Computer vision">computer vision</a>, introduced by researchers at Google in 2014 as GoogLeNet (later renamed Inception v1). The series was historically important as an early CNN that separates the stem (data ingest), body (data processing), and head (prediction), an architectural design that persists in all modern CNN.<sup id="cite_ref-:2_2-0" class="reference"><a href="#cite_note-:2-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup> </p> <figure class="mw-default-size" typeof="mw:File/Thumb"><a href="/wiki/File:Inception-v3_model.png" class="mw-file-description"><img src="//upload.wikimedia.org/wikipedia/commons/thumb/d/d7/Inception-v3_model.png/220px-Inception-v3_model.png" decoding="async" width="220" height="54" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/d/d7/Inception-v3_model.png/330px-Inception-v3_model.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/d/d7/Inception-v3_model.png/440px-Inception-v3_model.png 2x" data-file-width="1426" data-file-height="350" /></a><figcaption>Inception-v3 model</figcaption></figure> <meta property="mw:PageProp/toc" /> <div class="mw-heading mw-heading2"><h2 id="Version_history">Version history</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Inception_(deep_learning_architecture)&action=edit&section=1" title="Edit section: Version history"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <div class="mw-heading mw-heading3"><h3 id="Inception_v1">Inception v1</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Inception_(deep_learning_architecture)&action=edit&section=2" title="Edit section: Inception v1"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <figure typeof="mw:File/Thumb"><a href="/wiki/File:GoogLeNet_architecture.svg" class="mw-file-description"><img src="//upload.wikimedia.org/wikipedia/commons/thumb/4/44/GoogLeNet_architecture.svg/127px-GoogLeNet_architecture.svg.png" decoding="async" width="127" height="377" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/4/44/GoogLeNet_architecture.svg/190px-GoogLeNet_architecture.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/4/44/GoogLeNet_architecture.svg/254px-GoogLeNet_architecture.svg.png 2x" data-file-width="148" data-file-height="439" /></a><figcaption>GoogLeNet architecture</figcaption></figure> <p>In 2014, a team at Google developed the GoogLeNet architecture, an instance of which won the <a href="/wiki/ImageNet_Large_Scale_Visual_Recognition_Challenge" class="mw-redirect" title="ImageNet Large Scale Visual Recognition Challenge">ImageNet Large-Scale Visual Recognition Challenge</a> 2014 (ILSVRC14).<sup id="cite_ref-szegedy_1-1" class="reference"><a href="#cite_note-szegedy-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-3" class="reference"><a href="#cite_note-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup> </p><p>The name came from the <a href="/wiki/LeNet" title="LeNet">LeNet</a> of 1998, since both LeNet and GoogLeNet are CNNs. They also called it "Inception" after a "we need to go deeper" internet meme, a phrase from <a href="/wiki/Inception" title="Inception"><i>Inception</i> (2010)</a> the film.<sup id="cite_ref-szegedy_1-2" class="reference"><a href="#cite_note-szegedy-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup> Because later, more versions were released, the original Inception architecture was renamed again as "Inception v1". </p><p> The models and the code were released under Apache 2.0 license on GitHub.<sup id="cite_ref-4" class="reference"><a href="#cite_note-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup></p><figure class="mw-default-size" typeof="mw:File/Thumb"><a href="/wiki/File:Inception-v3_model_module.png" class="mw-file-description"><img src="//upload.wikimedia.org/wikipedia/commons/thumb/5/57/Inception-v3_model_module.png/220px-Inception-v3_model_module.png" decoding="async" width="220" height="69" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/5/57/Inception-v3_model_module.png/330px-Inception-v3_model_module.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/57/Inception-v3_model_module.png/440px-Inception-v3_model_module.png 2x" data-file-width="1426" data-file-height="450" /></a><figcaption>An individual Inception module. On the left is a standard module, and on the right is a dimension-reduced module.</figcaption></figure> <figure class="mw-default-size" typeof="mw:File/Thumb"><a href="/wiki/File:Inception_dimension-reduced_module.svg" class="mw-file-description"><img src="//upload.wikimedia.org/wikipedia/commons/thumb/c/c3/Inception_dimension-reduced_module.svg/220px-Inception_dimension-reduced_module.svg.png" decoding="async" width="220" height="66" class="mw-file-element" srcset="//upload.wikimedia.org/wikipedia/commons/thumb/c/c3/Inception_dimension-reduced_module.svg/330px-Inception_dimension-reduced_module.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/c/c3/Inception_dimension-reduced_module.svg/440px-Inception_dimension-reduced_module.svg.png 2x" data-file-width="517" data-file-height="155" /></a><figcaption>A single Inception dimension-reduced module</figcaption></figure> <p>The Inception v1 architecture is a deep CNN composed of 22 layers. Most of these layers were "Inception modules". The original paper stated that Inception modules are a "logical culmination" of <i>Network in Network</i><sup id="cite_ref-5" class="reference"><a href="#cite_note-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> and (Arora et al, 2014).<sup id="cite_ref-6" class="reference"><a href="#cite_note-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup> </p><p>Since Inception v1 is deep, it suffered from the <a href="/wiki/Vanishing_gradient_problem" title="Vanishing gradient problem">vanishing gradient problem</a>. The team solved it by using two "auxiliary classifiers", which are linear-softmax classifiers inserted at 1/3-deep and 2/3-deep within the network, and the loss function is a weighted sum of all three:<span class="mwe-math-element"><span class="mwe-math-mathml-display mwe-math-mathml-a11y" style="display: none;"><math display="block" xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle L=0.3L_{aux,1}+0.3L_{aux,2}+L_{real}}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>L</mi> <mo>=</mo> <mn>0.3</mn> <msub> <mi>L</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>a</mi> <mi>u</mi> <mi>x</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> <mo>+</mo> <mn>0.3</mn> <msub> <mi>L</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>a</mi> <mi>u</mi> <mi>x</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> <mo>+</mo> <msub> <mi>L</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>r</mi> <mi>e</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle L=0.3L_{aux,1}+0.3L_{aux,2}+L_{real}}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/32d9c73569238b2593b4e067f0983ac71976a46a" class="mwe-math-fallback-image-display mw-invert skin-invert" aria-hidden="true" style="vertical-align: -1.005ex; width:32.677ex; height:2.843ex;" alt="{\displaystyle L=0.3L_{aux,1}+0.3L_{aux,2}+L_{real}}" /></span> </p><p>These were removed after training was complete. This was later solved by the <a href="/wiki/Residual_neural_network" title="Residual neural network">ResNet architecture</a>. </p><p>The architecture consists of three parts stacked on top of one another:<sup id="cite_ref-:2_2-1" class="reference"><a href="#cite_note-:2-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup> </p> <ul><li>The stem (data ingestion): The first few convolutional layers perform data preprocessing to downscale images to a smaller size.</li> <li>The body (data processing): The next many Inception modules perform the bulk of data processing.</li> <li>The head (prediction): The final fully-connected layer and softmax produces a probability distribution for image classification.</li></ul> <p>This structure is used in most modern CNN architectures. </p> <div class="mw-heading mw-heading3"><h3 id="Inception_v2">Inception v2</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Inception_(deep_learning_architecture)&action=edit&section=3" title="Edit section: Inception v2"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>Inception v2 was released in 2015, in a paper that is more famous for proposing <a href="/wiki/Batch_normalization" title="Batch normalization">batch normalization</a>.<sup id="cite_ref-:0_7-0" class="reference"><a href="#cite_note-:0-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-8" class="reference"><a href="#cite_note-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup> It had 13.6 million parameters. </p><p>It improves on Inception v1 by adding batch normalization, and removing dropout and <a href="/wiki/Local_response_normalization" class="mw-redirect" title="Local response normalization">local response normalization</a> which they found became unnecessary when batch normalization is used. </p> <div class="mw-heading mw-heading3"><h3 id="Inception_v3">Inception v3</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Inception_(deep_learning_architecture)&action=edit&section=4" title="Edit section: Inception v3"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p><span class="anchor" id="Inception_v3"></span>Inception v3 was released in 2016.<sup id="cite_ref-:0_7-1" class="reference"><a href="#cite_note-:0-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-9" class="reference"><a href="#cite_note-9"><span class="cite-bracket">[</span>9<span class="cite-bracket">]</span></a></sup> It improves on Inception v2 by using factorized convolutions. </p><p>As an example, a single 5×5 convolution can be factored into 3×3 stacked on top of another 3×3. Both has a receptive field of size 5×5. The 5×5 convolution kernel has 25 parameters, compared to just 18 in the factorized version. Thus, the 5×5 convolution is strictly more powerful than the factorized version. However, this power is not necessarily needed. Empirically, the research team found that factorized convolutions help. </p><p>It also uses a form of dimension-reduction by concatenating the output from a convolutional layer and a <a href="/wiki/Pooling_layer" title="Pooling layer">pooling layer</a>. As an example, a tensor of size <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle 35\times 35\times 320}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mn>35</mn> <mo>×</mo> <mn>35</mn> <mo>×</mo> <mn>320</mn> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle 35\times 35\times 320}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/35c66e2f21264c4443c8dd6d6f65ca04d66d6379" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:13.818ex; height:2.176ex;" alt="{\displaystyle 35\times 35\times 320}" /></span> can be downscaled by a convolution with stride 2 to <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle 17\times 17\times 320}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mn>17</mn> <mo>×</mo> <mn>17</mn> <mo>×</mo> <mn>320</mn> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle 17\times 17\times 320}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/dace947c7339424b097fd1e5b4c2bd797ac87b0c" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:13.818ex; height:2.176ex;" alt="{\displaystyle 17\times 17\times 320}" /></span>, and by maxpooling with pool size <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle 2\times 2}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mn>2</mn> <mo>×</mo> <mn>2</mn> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle 2\times 2}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/f8a0e3400ffb97d67c00267ed50cddfe824cbe80" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:5.165ex; height:2.176ex;" alt="{\displaystyle 2\times 2}" /></span> to <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle 17\times 17\times 320}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mn>17</mn> <mo>×</mo> <mn>17</mn> <mo>×</mo> <mn>320</mn> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle 17\times 17\times 320}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/dace947c7339424b097fd1e5b4c2bd797ac87b0c" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:13.818ex; height:2.176ex;" alt="{\displaystyle 17\times 17\times 320}" /></span>. These are then concatenated to <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle 17\times 17\times 640}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mn>17</mn> <mo>×</mo> <mn>17</mn> <mo>×</mo> <mn>640</mn> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle 17\times 17\times 640}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/fdee55dc6bbdaba1c8cb91b5be7f57a938350070" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:13.818ex; height:2.176ex;" alt="{\displaystyle 17\times 17\times 640}" /></span>. </p><p>Other than this, it also removed the lowest auxiliary classifier during training. They found that the auxiliary head worked as a form of <a href="/wiki/Regularization_(machine_learning)" class="mw-redirect" title="Regularization (machine learning)">regularization</a>. </p><p>They also proposed label-smoothing regularization in classification. For an image with label <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle c}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>c</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle c}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/86a67b81c2de995bd608d5b2df50cd8cd7d92455" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.007ex; height:1.676ex;" alt="{\displaystyle c}" /></span>, instead of making the model to predict the probability distribution <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \delta _{c}=(0,0,\dots ,0,\underbrace {1} _{c{\text{-th entry}}},0,\dots ,0)}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <msub> <mi>δ</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mo stretchy="false">(</mo> <mn>0</mn> <mo>,</mo> <mn>0</mn> <mo>,</mo> <mo>…</mo> <mo>,</mo> <mn>0</mn> <mo>,</mo> <munder> <mrow class="MJX-TeXAtom-OP MJX-fixedlimits"> <munder> <mn>1</mn> <mo>⏟</mo> </munder> </mrow> <mrow class="MJX-TeXAtom-ORD"> <mi>c</mi> <mrow class="MJX-TeXAtom-ORD"> <mtext>-th entry</mtext> </mrow> </mrow> </munder> <mo>,</mo> <mn>0</mn> <mo>,</mo> <mo>…</mo> <mo>,</mo> <mn>0</mn> <mo stretchy="false">)</mo> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle \delta _{c}=(0,0,\dots ,0,\underbrace {1} _{c{\text{-th entry}}},0,\dots ,0)}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/520f522e5b6a913fc8cc45ea16ba2586776c31ee" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -4.005ex; width:33.345ex; height:6.009ex;" alt="{\displaystyle \delta _{c}=(0,0,\dots ,0,\underbrace {1} _{c{\text{-th entry}}},0,\dots ,0)}" /></span>, they made the model predict the smoothed distribution <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle (1-\epsilon )\delta _{c}+\epsilon /K}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mo stretchy="false">(</mo> <mn>1</mn> <mo>−</mo> <mi>ϵ</mi> <mo stretchy="false">)</mo> <msub> <mi>δ</mi> <mrow class="MJX-TeXAtom-ORD"> <mi>c</mi> </mrow> </msub> <mo>+</mo> <mi>ϵ</mi> <mrow class="MJX-TeXAtom-ORD"> <mo>/</mo> </mrow> <mi>K</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle (1-\epsilon )\delta _{c}+\epsilon /K}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/5cc75a774a1478e89b032ae31be1090ed564c332" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:15.746ex; height:2.843ex;" alt="{\displaystyle (1-\epsilon )\delta _{c}+\epsilon /K}" /></span> where <span class="mwe-math-element"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle K}"> <semantics> <mrow class="MJX-TeXAtom-ORD"> <mstyle displaystyle="true" scriptlevel="0"> <mi>K</mi> </mstyle> </mrow> <annotation encoding="application/x-tex">{\displaystyle K}</annotation> </semantics> </math></span><img src="https://wikimedia.org/api/rest_v1/media/math/render/svg/2b76fce82a62ed5461908f0dc8f037de4e3686b0" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:2.066ex; height:2.176ex;" alt="{\displaystyle K}" /></span> is the total number of classes. </p> <div class="mw-heading mw-heading3"><h3 id="Inception_v4">Inception v4</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Inception_(deep_learning_architecture)&action=edit&section=5" title="Edit section: Inception v4"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>In 2017, the team released Inception v4, Inception ResNet v1, and Inception ResNet v2.<sup id="cite_ref-:1_10-0" class="reference"><a href="#cite_note-:1-10"><span class="cite-bracket">[</span>10<span class="cite-bracket">]</span></a></sup> </p><p>Inception v4 is an incremental update with even more factorized convolutions, and other complications that were empirically found to improve benchmarks. </p><p>Inception ResNet v1 and v2 are both modifications of Inception v4, where <a href="/wiki/Residual_neural_network" title="Residual neural network">residual connections</a> are added to each Inception module, inspired by the <a href="/wiki/Residual_neural_network" title="Residual neural network">ResNet</a> architecture.<sup id="cite_ref-resnet_11-0" class="reference"><a href="#cite_note-resnet-11"><span class="cite-bracket">[</span>11<span class="cite-bracket">]</span></a></sup> </p> <div class="mw-heading mw-heading3"><h3 id="Xception">Xception</h3><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Inception_(deep_learning_architecture)&action=edit&section=6" title="Edit section: Xception"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <p>Xception ("Extreme Inception") was published in 2017.<sup id="cite_ref-12" class="reference"><a href="#cite_note-12"><span class="cite-bracket">[</span>12<span class="cite-bracket">]</span></a></sup> It is a linear stack of <a href="/wiki/Depthwise_separable_convolution" class="mw-redirect" title="Depthwise separable convolution">depthwise separable convolution</a> layers with residual connections. The design was proposed on the hypothesis that in a CNN, the cross-channels correlations and spatial correlations in the feature maps can be <i>entirely decoupled</i>. </p> <div class="mw-heading mw-heading2"><h2 id="References">References</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Inception_(deep_learning_architecture)&action=edit&section=7" title="Edit section: References"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <style data-mw-deduplicate="TemplateStyles:r1239543626">.mw-parser-output .reflist{margin-bottom:0.5em;list-style-type:decimal}@media screen{.mw-parser-output .reflist{font-size:90%}}.mw-parser-output .reflist .references{font-size:100%;margin-bottom:0;list-style-type:inherit}.mw-parser-output .reflist-columns-2{column-width:30em}.mw-parser-output .reflist-columns-3{column-width:25em}.mw-parser-output .reflist-columns{margin-top:0.3em}.mw-parser-output .reflist-columns ol{margin-top:0}.mw-parser-output .reflist-columns li{page-break-inside:avoid;break-inside:avoid-column}.mw-parser-output .reflist-upper-alpha{list-style-type:upper-alpha}.mw-parser-output .reflist-upper-roman{list-style-type:upper-roman}.mw-parser-output .reflist-lower-alpha{list-style-type:lower-alpha}.mw-parser-output .reflist-lower-greek{list-style-type:lower-greek}.mw-parser-output .reflist-lower-roman{list-style-type:lower-roman}</style><div class="reflist"> <div class="mw-references-wrap mw-references-columns"><ol class="references"> <li id="cite_note-szegedy-1"><span class="mw-cite-backlink">^ <a href="#cite_ref-szegedy_1-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-szegedy_1-1"><sup><i><b>b</b></i></sup></a> <a href="#cite_ref-szegedy_1-2"><sup><i><b>c</b></i></sup></a></span> <span class="reference-text"><style data-mw-deduplicate="TemplateStyles:r1238218222">.mw-parser-output cite.citation{font-style:inherit;word-wrap:break-word}.mw-parser-output .citation q{quotes:"\"""\"""'""'"}.mw-parser-output .citation:target{background-color:rgba(0,127,255,0.133)}.mw-parser-output .id-lock-free.id-lock-free a{background:url("//upload.wikimedia.org/wikipedia/commons/6/65/Lock-green.svg")right 0.1em center/9px no-repeat}.mw-parser-output .id-lock-limited.id-lock-limited a,.mw-parser-output .id-lock-registration.id-lock-registration a{background:url("//upload.wikimedia.org/wikipedia/commons/d/d6/Lock-gray-alt-2.svg")right 0.1em center/9px no-repeat}.mw-parser-output .id-lock-subscription.id-lock-subscription a{background:url("//upload.wikimedia.org/wikipedia/commons/a/aa/Lock-red-alt-2.svg")right 0.1em center/9px no-repeat}.mw-parser-output .cs1-ws-icon a{background:url("//upload.wikimedia.org/wikipedia/commons/4/4c/Wikisource-logo.svg")right 0.1em center/12px no-repeat}body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-free a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-limited a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-registration a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .id-lock-subscription a,body:not(.skin-timeless):not(.skin-minerva) .mw-parser-output .cs1-ws-icon a{background-size:contain;padding:0 1em 0 0}.mw-parser-output .cs1-code{color:inherit;background:inherit;border:none;padding:inherit}.mw-parser-output .cs1-hidden-error{display:none;color:var(--color-error,#d33)}.mw-parser-output .cs1-visible-error{color:var(--color-error,#d33)}.mw-parser-output .cs1-maint{display:none;color:#085;margin-left:0.3em}.mw-parser-output .cs1-kern-left{padding-left:0.2em}.mw-parser-output .cs1-kern-right{padding-right:0.2em}.mw-parser-output .citation .mw-selflink{font-weight:inherit}@media screen{.mw-parser-output .cs1-format{font-size:95%}html.skin-theme-clientpref-night .mw-parser-output .cs1-maint{color:#18911f}}@media screen and (prefers-color-scheme:dark){html.skin-theme-clientpref-os .mw-parser-output .cs1-maint{color:#18911f}}</style><cite id="CITEREFSzegedyWei_LiuYangqing_JiaSermanet2015" class="citation book cs1">Szegedy, Christian; Wei Liu; Yangqing Jia; Sermanet, Pierre; Reed, Scott; Anguelov, Dragomir; Erhan, Dumitru; Vanhoucke, Vincent; Rabinovich, Andrew (June 2015). <a rel="nofollow" class="external text" href="https://ieeexplore.ieee.org/document/7298594">"Going deeper with convolutions"</a>. <i>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</i>. IEEE. pp. <span class="nowrap">1–</span>9. <a href="/wiki/ArXiv_(identifier)" class="mw-redirect" title="ArXiv (identifier)">arXiv</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://arxiv.org/abs/1409.4842">1409.4842</a></span>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1109%2FCVPR.2015.7298594">10.1109/CVPR.2015.7298594</a>. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-1-4673-6964-0" title="Special:BookSources/978-1-4673-6964-0"><bdi>978-1-4673-6964-0</bdi></a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=Going+deeper+with+convolutions&rft.btitle=2015+IEEE+Conference+on+Computer+Vision+and+Pattern+Recognition+%28CVPR%29&rft.pages=%3Cspan+class%3D%22nowrap%22%3E1-%3C%2Fspan%3E9&rft.pub=IEEE&rft.date=2015-06&rft_id=info%3Aarxiv%2F1409.4842&rft_id=info%3Adoi%2F10.1109%2FCVPR.2015.7298594&rft.isbn=978-1-4673-6964-0&rft.aulast=Szegedy&rft.aufirst=Christian&rft.au=Wei+Liu&rft.au=Yangqing+Jia&rft.au=Sermanet%2C+Pierre&rft.au=Reed%2C+Scott&rft.au=Anguelov%2C+Dragomir&rft.au=Erhan%2C+Dumitru&rft.au=Vanhoucke%2C+Vincent&rft.au=Rabinovich%2C+Andrew&rft_id=https%3A%2F%2Fieeexplore.ieee.org%2Fdocument%2F7298594&rfr_id=info%3Asid%2Fen.wikipedia.org%3AInception+%28deep+learning+architecture%29" class="Z3988"></span></span> </li> <li id="cite_note-:2-2"><span class="mw-cite-backlink">^ <a href="#cite_ref-:2_2-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-:2_2-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFZhangLiptonLiSmola2024" class="citation book cs1">Zhang, Aston; Lipton, Zachary; Li, Mu; Smola, Alexander J. 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Cambridge New York Port Melbourne New Delhi Singapore: Cambridge University Press. <a href="/wiki/ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <a href="/wiki/Special:BookSources/978-1-009-38943-3" title="Special:BookSources/978-1-009-38943-3"><bdi>978-1-009-38943-3</bdi></a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=bookitem&rft.atitle=8.4.+Multi-Branch+Networks+%28GoogLeNet%29&rft.btitle=Dive+into+deep+learning&rft.place=Cambridge+New+York+Port+Melbourne+New+Delhi+Singapore&rft.pub=Cambridge+University+Press&rft.date=2024&rft.isbn=978-1-009-38943-3&rft.aulast=Zhang&rft.aufirst=Aston&rft.au=Lipton%2C+Zachary&rft.au=Li%2C+Mu&rft.au=Smola%2C+Alexander+J.&rft_id=https%3A%2F%2Fd2l.ai%2Fchapter_convolutional-modern%2Fgooglenet.html&rfr_id=info%3Asid%2Fen.wikipedia.org%3AInception+%28deep+learning+architecture%29" class="Z3988"></span></span> </li> <li id="cite_note-3"><span class="mw-cite-backlink"><b><a href="#cite_ref-3">^</a></b></span> <span class="reference-text"><a rel="nofollow" class="external text" href="https://www.kaggle.com/models/google/inception-v1">Official repo of Inception V1 on Kaggle, published by Google.</a></span> </li> <li id="cite_note-4"><span class="mw-cite-backlink"><b><a href="#cite_ref-4">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://github.com/google/inception?tab=readme-ov-file">"google/inception"</a>. Google. 2024-08-19<span class="reference-accessdate">. Retrieved <span class="nowrap">2024-08-19</span></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=unknown&rft.btitle=google%2Finception&rft.pub=Google&rft.date=2024-08-19&rft_id=https%3A%2F%2Fgithub.com%2Fgoogle%2Finception%3Ftab%3Dreadme-ov-file&rfr_id=info%3Asid%2Fen.wikipedia.org%3AInception+%28deep+learning+architecture%29" class="Z3988"></span></span> </li> <li id="cite_note-5"><span class="mw-cite-backlink"><b><a href="#cite_ref-5">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFLinChenYan2014" class="citation arxiv cs1">Lin, Min; Chen, Qiang; Yan, Shuicheng (2014-03-04). "Network In Network". <a href="/wiki/ArXiv_(identifier)" class="mw-redirect" title="ArXiv (identifier)">arXiv</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://arxiv.org/abs/1312.4400">1312.4400</a></span> [<a rel="nofollow" class="external text" href="https://arxiv.org/archive/cs.NE">cs.NE</a>].</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=preprint&rft.jtitle=arXiv&rft.atitle=Network+In+Network&rft.date=2014-03-04&rft_id=info%3Aarxiv%2F1312.4400&rft.aulast=Lin&rft.aufirst=Min&rft.au=Chen%2C+Qiang&rft.au=Yan%2C+Shuicheng&rfr_id=info%3Asid%2Fen.wikipedia.org%3AInception+%28deep+learning+architecture%29" class="Z3988"></span></span> </li> <li id="cite_note-6"><span class="mw-cite-backlink"><b><a href="#cite_ref-6">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFAroraBhaskaraGeMa2014" class="citation journal cs1">Arora, Sanjeev; Bhaskara, Aditya; Ge, Rong; Ma, Tengyu (2014-01-27). <a rel="nofollow" class="external text" href="https://proceedings.mlr.press/v32/arora14.html">"Provable Bounds for Learning Some Deep Representations"</a>. <i>Proceedings of the 31st International Conference on Machine Learning</i>. PMLR: <span class="nowrap">584–</span>592.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Proceedings+of+the+31st+International+Conference+on+Machine+Learning&rft.atitle=Provable+Bounds+for+Learning+Some+Deep+Representations&rft.pages=%3Cspan+class%3D%22nowrap%22%3E584-%3C%2Fspan%3E592&rft.date=2014-01-27&rft.aulast=Arora&rft.aufirst=Sanjeev&rft.au=Bhaskara%2C+Aditya&rft.au=Ge%2C+Rong&rft.au=Ma%2C+Tengyu&rft_id=https%3A%2F%2Fproceedings.mlr.press%2Fv32%2Farora14.html&rfr_id=info%3Asid%2Fen.wikipedia.org%3AInception+%28deep+learning+architecture%29" class="Z3988"></span></span> </li> <li id="cite_note-:0-7"><span class="mw-cite-backlink">^ <a href="#cite_ref-:0_7-0"><sup><i><b>a</b></i></sup></a> <a href="#cite_ref-:0_7-1"><sup><i><b>b</b></i></sup></a></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFSzegedyVanhouckeIoffeShlens2016" class="citation journal cs1">Szegedy, Christian; Vanhoucke, Vincent; Ioffe, Sergey; Shlens, Jon; Wojna, Zbigniew (2016). <a rel="nofollow" class="external text" href="https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.html">"Rethinking the Inception Architecture for Computer Vision"</a>. <i>Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</i>: <span class="nowrap">2818–</span>2826.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Proceedings+of+the+IEEE+Conference+on+Computer+Vision+and+Pattern+Recognition+%28CVPR%29&rft.atitle=Rethinking+the+Inception+Architecture+for+Computer+Vision&rft.pages=%3Cspan+class%3D%22nowrap%22%3E2818-%3C%2Fspan%3E2826&rft.date=2016&rft.aulast=Szegedy&rft.aufirst=Christian&rft.au=Vanhoucke%2C+Vincent&rft.au=Ioffe%2C+Sergey&rft.au=Shlens%2C+Jon&rft.au=Wojna%2C+Zbigniew&rft_id=https%3A%2F%2Fwww.cv-foundation.org%2Fopenaccess%2Fcontent_cvpr_2016%2Fhtml%2FSzegedy_Rethinking_the_Inception_CVPR_2016_paper.html&rfr_id=info%3Asid%2Fen.wikipedia.org%3AInception+%28deep+learning+architecture%29" class="Z3988"></span></span> </li> <li id="cite_note-8"><span class="mw-cite-backlink"><b><a href="#cite_ref-8">^</a></b></span> <span class="reference-text"><a rel="nofollow" class="external text" href="https://www.kaggle.com/models/google/inception-v2">Official repo of Inception V2 on Kaggle, published by Google.</a></span> </li> <li id="cite_note-9"><span class="mw-cite-backlink"><b><a href="#cite_ref-9">^</a></b></span> <span class="reference-text"><a rel="nofollow" class="external text" href="https://www.kaggle.com/models/google/inception-v3/">Official repo of Inception V3 on Kaggle, published by Google.</a></span> </li> <li id="cite_note-:1-10"><span class="mw-cite-backlink"><b><a href="#cite_ref-:1_10-0">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFSzegedyIoffeVanhouckeAlemi2017" class="citation journal cs1">Szegedy, Christian; Ioffe, Sergey; Vanhoucke, Vincent; Alemi, Alexander (2017-02-12). <a rel="nofollow" class="external text" href="https://ojs.aaai.org/index.php/aaai/article/view/11231">"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning"</a>. <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>. <b>31</b> (1). <a href="/wiki/ArXiv_(identifier)" class="mw-redirect" title="ArXiv (identifier)">arXiv</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://arxiv.org/abs/1602.07261">1602.07261</a></span>. <a href="/wiki/Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1609%2Faaai.v31i1.11231">10.1609/aaai.v31i1.11231</a>. <a href="/wiki/ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/2374-3468">2374-3468</a>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Proceedings+of+the+AAAI+Conference+on+Artificial+Intelligence&rft.atitle=Inception-v4%2C+Inception-ResNet+and+the+Impact+of+Residual+Connections+on+Learning&rft.volume=31&rft.issue=1&rft.date=2017-02-12&rft_id=info%3Aarxiv%2F1602.07261&rft.issn=2374-3468&rft_id=info%3Adoi%2F10.1609%2Faaai.v31i1.11231&rft.aulast=Szegedy&rft.aufirst=Christian&rft.au=Ioffe%2C+Sergey&rft.au=Vanhoucke%2C+Vincent&rft.au=Alemi%2C+Alexander&rft_id=https%3A%2F%2Fojs.aaai.org%2Findex.php%2Faaai%2Farticle%2Fview%2F11231&rfr_id=info%3Asid%2Fen.wikipedia.org%3AInception+%28deep+learning+architecture%29" class="Z3988"></span></span> </li> <li id="cite_note-resnet-11"><span class="mw-cite-backlink"><b><a href="#cite_ref-resnet_11-0">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFHeZhangRenSun2015" class="citation conference cs1">He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing; Sun, Jian (10 Dec 2015). <i>Deep Residual Learning for Image Recognition</i>. <a href="/wiki/ArXiv_(identifier)" class="mw-redirect" title="ArXiv (identifier)">arXiv</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://arxiv.org/abs/1512.03385">1512.03385</a></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=conference&rft.btitle=Deep+Residual+Learning+for+Image+Recognition&rft.date=2015-12-10&rft_id=info%3Aarxiv%2F1512.03385&rft.aulast=He&rft.aufirst=Kaiming&rft.au=Zhang%2C+Xiangyu&rft.au=Ren%2C+Shaoqing&rft.au=Sun%2C+Jian&rfr_id=info%3Asid%2Fen.wikipedia.org%3AInception+%28deep+learning+architecture%29" class="Z3988"></span></span> </li> <li id="cite_note-12"><span class="mw-cite-backlink"><b><a href="#cite_ref-12">^</a></b></span> <span class="reference-text"><link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite id="CITEREFChollet2017" class="citation journal cs1">Chollet, Francois (2017). <a rel="nofollow" class="external text" href="https://openaccess.thecvf.com/content_cvpr_2017/html/Chollet_Xception_Deep_Learning_CVPR_2017_paper.html">"Xception: Deep Learning With Depthwise Separable Convolutions"</a>. <i>Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</i>: <span class="nowrap">1251–</span>1258.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.jtitle=Proceedings+of+the+IEEE+Conference+on+Computer+Vision+and+Pattern+Recognition+%28CVPR%29&rft.atitle=Xception%3A+Deep+Learning+With+Depthwise+Separable+Convolutions&rft.pages=%3Cspan+class%3D%22nowrap%22%3E1251-%3C%2Fspan%3E1258&rft.date=2017&rft.aulast=Chollet&rft.aufirst=Francois&rft_id=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_cvpr_2017%2Fhtml%2FChollet_Xception_Deep_Learning_CVPR_2017_paper.html&rfr_id=info%3Asid%2Fen.wikipedia.org%3AInception+%28deep+learning+architecture%29" class="Z3988"></span></span> </li> </ol></div></div> <div class="mw-heading mw-heading2"><h2 id="External_links">External links</h2><span class="mw-editsection"><span class="mw-editsection-bracket">[</span><a href="/w/index.php?title=Inception_(deep_learning_architecture)&action=edit&section=8" title="Edit section: External links"><span>edit</span></a><span class="mw-editsection-bracket">]</span></span></div> <ul><li>A list of all Inception models released by Google: <link rel="mw-deduplicated-inline-style" href="mw-data:TemplateStyles:r1238218222" /><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://github.com/tensorflow/models/blob/master/research/slim/README.md#pre-trained-models">"models/research/slim/README.md at master · tensorflow/models"</a>. <i>GitHub</i><span class="reference-accessdate">. Retrieved <span class="nowrap">2024-10-19</span></span>.</cite><span title="ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=unknown&rft.jtitle=GitHub&rft.atitle=models%2Fresearch%2Fslim%2FREADME.md+at+master+%C2%B7+tensorflow%2Fmodels&rft_id=https%3A%2F%2Fgithub.com%2Ftensorflow%2Fmodels%2Fblob%2Fmaster%2Fresearch%2Fslim%2FREADME.md%23pre-trained-models&rfr_id=info%3Asid%2Fen.wikipedia.org%3AInception+%28deep+learning+architecture%29" class="Z3988"></span></li></ul> <div class="navbox-styles"><style data-mw-deduplicate="TemplateStyles:r1129693374">.mw-parser-output .hlist dl,.mw-parser-output .hlist ol,.mw-parser-output .hlist ul{margin:0;padding:0}.mw-parser-output .hlist dd,.mw-parser-output .hlist dt,.mw-parser-output .hlist li{margin:0;display:inline}.mw-parser-output .hlist.inline,.mw-parser-output .hlist.inline dl,.mw-parser-output .hlist.inline ol,.mw-parser-output .hlist.inline ul,.mw-parser-output .hlist dl dl,.mw-parser-output .hlist dl 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class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"></div><table class="nowraplinks navbox-subgroup" style="border-spacing:0"><tbody><tr><th scope="row" class="navbox-group" style="width:1%">Versions</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/AlphaGo" title="AlphaGo">AlphaGo</a> (2015)</li> <li><a href="/wiki/Master_(software)" title="Master (software)">Master</a> (2016)</li> <li><a href="/wiki/AlphaGo_Zero" title="AlphaGo Zero">AlphaGo Zero</a> (2017)</li> <li><a href="/wiki/AlphaZero" title="AlphaZero">AlphaZero</a> (2017)</li> <li><a href="/wiki/MuZero" title="MuZero">MuZero</a> (2019)</li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Competitions</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/AlphaGo_versus_Fan_Hui" title="AlphaGo versus Fan Hui">Fan Hui</a> (2015)</li> <li><a href="/wiki/AlphaGo_versus_Lee_Sedol" title="AlphaGo versus Lee Sedol">Lee Sedol</a> (2016)</li> <li><a href="/wiki/AlphaGo_versus_Ke_Jie" title="AlphaGo versus Ke Jie">Ke Jie</a> (2017)</li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">In popular culture</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><i><a href="/wiki/AlphaGo_(film)" title="AlphaGo (film)">AlphaGo</a></i> (2017)</li> <li><i><a href="/wiki/The_MANIAC" title="The MANIAC">The MANIAC</a></i> (2023)</li></ul> </div></td></tr></tbody></table><div></div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Other</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/AlphaFold" title="AlphaFold">AlphaFold</a> (2018)</li> <li><a href="/wiki/AlphaStar_(software)" title="AlphaStar (software)">AlphaStar</a> (2019)</li> <li><a href="/wiki/AlphaDev" title="AlphaDev">AlphaDev</a> (2023)</li> <li><a href="/wiki/AlphaGeometry" title="AlphaGeometry">AlphaGeometry</a> (2024)</li></ul> </div></td></tr></tbody></table><div></div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Machine learning</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"></div><table class="nowraplinks navbox-subgroup" style="border-spacing:0"><tbody><tr><th scope="row" class="navbox-group" style="width:1%">Neural networks</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a class="mw-selflink selflink">Inception</a> (2014)</li> <li><a href="/wiki/WaveNet" title="WaveNet">WaveNet</a> (2016)</li> <li><a href="/wiki/MobileNet" title="MobileNet">MobileNet</a> (2017)</li> <li><a href="/wiki/Transformer_(deep_learning_architecture)" title="Transformer (deep learning architecture)">Transformer</a> (2017)</li> <li><a href="/wiki/EfficientNet" title="EfficientNet">EfficientNet</a> (2019)</li> <li><a href="/wiki/Gato_(DeepMind)" title="Gato (DeepMind)">Gato</a> (2022)</li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Other</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Quantum_Artificial_Intelligence_Lab" title="Quantum Artificial Intelligence Lab">Quantum Artificial Intelligence Lab</a></li> <li><a href="/wiki/TensorFlow" title="TensorFlow">TensorFlow</a></li> <li><a href="/wiki/Tensor_Processing_Unit" title="Tensor Processing Unit">Tensor Processing Unit</a></li></ul> </div></td></tr></tbody></table><div></div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Generative AI</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"></div><table class="nowraplinks navbox-subgroup" style="border-spacing:0"><tbody><tr><th scope="row" class="navbox-group" style="width:1%">Chatbots</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/Google_Assistant" title="Google Assistant">Assistant</a> (2016)</li> <li><a href="/wiki/Sparrow_(chatbot)" title="Sparrow (chatbot)">Sparrow</a> (2022)</li> <li><a href="/wiki/Gemini_(chatbot)" title="Gemini (chatbot)">Gemini</a> (2023)</li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Language models</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/BERT_(language_model)" title="BERT (language model)">BERT</a> (2018)</li> <li><a href="/wiki/XLNet" title="XLNet">XLNet</a> (2019)</li> <li><a href="/wiki/T5_(language_model)" title="T5 (language model)">T5</a> (2019)</li> <li><a href="/wiki/LaMDA" title="LaMDA">LaMDA</a> (2021)</li> <li><a href="/wiki/Chinchilla_(language_model)" title="Chinchilla (language model)">Chinchilla</a> (2022)</li> <li><a href="/wiki/PaLM" title="PaLM">PaLM</a> (2022)</li> <li><a href="/wiki/Gemini_(language_model)" title="Gemini (language model)">Gemini</a> (2023)</li> <li><a href="/wiki/VideoPoet" title="VideoPoet">VideoPoet</a> (2024)</li></ul> </div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Other</th><td class="navbox-list-with-group navbox-list navbox-odd" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li><a href="/wiki/DreamBooth" title="DreamBooth">DreamBooth</a> (2022)</li> <li><a href="/wiki/NotebookLM" title="NotebookLM">NotebookLM</a> (2023)</li> <li><a href="/wiki/Google_Vids" title="Google Vids">Vids</a> (2024)</li></ul> </div></td></tr></tbody></table><div></div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">See also</th><td class="navbox-list-with-group navbox-list navbox-even" style="width:100%;padding:0"><div style="padding:0 0.25em"> <ul><li>"<a href="/wiki/Attention_Is_All_You_Need" title="Attention Is All You Need">Attention Is All You Need</a>"</li> <li><a href="/wiki/Future_of_Go_Summit" title="Future of Go Summit">Future of Go Summit</a></li> <li><a href="/wiki/Generative_pre-trained_transformer" title="Generative pre-trained transformer">Generative pre-trained transformer</a></li> <li><a href="/wiki/Google_Labs" title="Google Labs">Google Labs</a></li> <li><a href="/wiki/Google_Pixel" title="Google Pixel">Google Pixel</a></li> <li><a href="/wiki/Google_Workspace" title="Google Workspace">Google Workspace</a></li> <li><a href="/wiki/Robot_Constitution" title="Robot Constitution">Robot Constitution</a></li></ul> </div></td></tr><tr><td class="navbox-abovebelow" 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