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href="https://proceedings.neurips.cc/paper_files/paper/2021/file/652cf38361a209088302ba2b8b7f51e0-Paper.pdf" data-clk="hl=en&sa=T&oi=gga&ct=gga&cd=0&d=1018521690946850362&ei=P0pBZ6z_O8ak6rQPkq38qAg" data-clk-atid="Os7q4BSEIg4J"><span class=gs_ctg2>[PDF]</span> neurips.cc</a></div></div></div><div class="gs_ri"><h3 class="gs_rt" ontouchstart="gs_evt_dsp(event)"><a id="Os7q4BSEIg4J" href="https://proceedings.neurips.cc/paper_files/paper/2021/hash/652cf38361a209088302ba2b8b7f51e0-Abstract.html" data-clk="hl=en&sa=T&ct=res&cd=0&d=1018521690946850362&ei=P0pBZ6z_O8ak6rQPkq38qAg" data-clk-atid="Os7q4BSEIg4J">Do vision transformers see like convolutional neural networks?</a></h3><div class="gs_a gs_fma_s"><a href="/citations?user=xdwK2NsAAAAJ&hl=en&oi=sra">M Raghu</a>, <a href="/citations?user=QCARd5gAAAAJ&hl=en&oi=sra">T Unterthiner</a>, <a href="/citations?user=1O3RPmsAAAAJ&hl=en&oi=sra">S Kornblith</a>… - Advances in neural …, 2021 - proceedings.neurips.cc</div><div class="gs_a gs_fma_p"><div class="gs_fmaa"><a href="/citations?user=xdwK2NsAAAAJ&hl=en&oi=sra">M Raghu</a>, <a href="/citations?user=QCARd5gAAAAJ&hl=en&oi=sra">T Unterthiner</a>, <a href="/citations?user=1O3RPmsAAAAJ&hl=en&oi=sra">S Kornblith</a>, <a href="/citations?user=l_G2vr0AAAAJ&hl=en&oi=sra">C Zhang</a>, <a href="/citations?user=FXNJRDoAAAAJ&hl=en&oi=sra">A Dosovitskiy</a></div>Advances in neural information processing systems, 2021<span class="gs_pdot">•</span>proceedings.neurips.cc</div><div class="gs_rs gs_fma_s">Convolutional neural networks (CNNs) have so far been the de-facto model for visual data. <br>Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or <br>even superior performance on image classification tasks. This raises a central question: how <br>are Vision Transformers solving these tasks? Are they acting like convolutional networks, or <br>learning entirely different visual representations? Analyzing the internal representation <br>structure of ViTs and CNNs on image classification benchmarks, we find striking differences …</div><div class="gs_fma gs_fma_p gs_invis"><div class="gs_fma_brdr"></div><div class="gs_fma_wpr"><div class="gs_fma_con gs_fma_sh" data-trk="/scholar_url?url=https://proceedings.neurips.cc/paper_files/paper/2021/hash/652cf38361a209088302ba2b8b7f51e0-Abstract.html&hl=en&sa=T&oi=fma-abs-view&ct=fma-expand&d=1018521690946850362&ei=P0pBZ6z_O8ak6rQPkq38qAg&tab=-1&at=Do+vision+transformers+see+like+convolutional+neural+networks%3F"><div class="gs_fma_hdr">Abstract</div><div class="gs_fma_abs"><div class="gs_fma_grad"></div><div class="gs_fma_snp"><div class="gsh_csp">Convolutional neural networks (CNNs) have so far been the de-facto model for visual data. Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or even superior performance on image classification tasks. This raises a central question: how are Vision Transformers solving these tasks? Are they acting like convolutional networks, or learning entirely different visual representations? Analyzing the internal representation structure of ViTs and CNNs on image classification benchmarks, we find striking differences between the two architectures, such as ViT having more uniform representations across all layers. We explore how these differences arise, finding crucial roles played by self-attention, which enables early aggregation of global information, and ViT residual connections, which strongly propagate features from lower to higher layers. We study the ramifications for spatial localization, demonstrating ViTs successfully preserve input spatial information, with noticeable effects from different classification methods. Finally, we study the effect of (pretraining) dataset scale on intermediate features and transfer learning, and conclude with a discussion on connections to new architectures such as the MLP-Mixer.</div></div><div class="gs_fma_fons"><div class="gs_fma_fon">proceedings.neurips.cc</div></div></div><div class="gs_fma_sml"><a class="gs_fma_sml_a" data-idx="0" href="javascript:void(0)" role="presentation"><span class="gs_fma_smlsm">Show more</span><span class="gs_fma_smlsl">Show less</span><span class="gs_fma_smlar"></span></a></div></div></div></div><div class="gs_fl gs_flb gs_invis"><a href="javascript:void(0)" class="gs_or_sav gs_or_btn" role="button"><svg viewBox="0 0 15 16" class="gs_or_svg"><path d="M7.5 11.57l3.824 2.308-1.015-4.35 3.379-2.926-4.45-.378L7.5 2.122 5.761 6.224l-4.449.378 3.379 2.926-1.015 4.35z"/></svg><span class="gs_or_btn_lbl">Save</span></a> <a href="javascript:void(0)" class="gs_or_cit gs_or_btn gs_nph" role="button" aria-controls="gs_cit" aria-haspopup="true"><svg viewBox="0 0 15 16" class="gs_or_svg"><path d="M6.5 3.5H1.5V8.5H3.75L1.75 12.5H4.75L6.5 9V3.5zM13.5 3.5H8.5V8.5H10.75L8.75 12.5H11.75L13.5 9V3.5z"/></svg><span>Cite</span></a> <a href="/scholar?cites=1018521690946850362&as_sdt=2005&sciodt=0,5&hl=en">Cited by 1080</a> <a href="/scholar?q=related:Os7q4BSEIg4J:scholar.google.com/&scioq=&hl=en&as_sdt=0,5">Related articles</a> <a href="/scholar?cluster=1018521690946850362&hl=en&as_sdt=0,5" class="gs_nph">All 8 versions</a> <a href="javascript:void(0)" title="More" class="gs_or_mor" role="button"><svg viewBox="0 0 15 16" class="gs_or_svg"><path d="M0.75 5.5l2-2L7.25 8l-4.5 4.5-2-2L3.25 8zM7.75 5.5l2-2L14.25 8l-4.5 4.5-2-2L10.25 8z"/></svg></a> <a href="https://scholar.googleusercontent.com/scholar?q=cache:Os7q4BSEIg4J:scholar.google.com/&hl=en&as_sdt=0,5" class="gs_or_nvi">View as HTML</a> <a href="javascript:void(0)" title="Fewer" class="gs_or_nvi gs_or_mor" role="button"><svg viewBox="0 0 15 16" class="gs_or_svg"><path d="M7.25 5.5l-2-2L0.75 8l4.5 4.5 2-2L4.75 8zM14.25 5.5l-2-2L7.75 8l4.5 4.5 2-2L11.75 8z"/></svg></a></div></div></div></div><div id="gs_res_ccl_bot" class="gs_fmarb gs_invis"><div style="height:1em"></div></div></div></div></div><div id="gs_ftr_sp" role="presentation"></div><div id="gs_ftr" class="gs_md_rmb" role="contentinfo"><div id="gs_ftr_rt"><a href="//www.google.com/intl/en/policies/privacy/">Privacy</a><a href="//www.google.com/intl/en/policies/terms/">Terms</a><a href="javascript:void(0)" ontouchstart="gs_evt_dsp(event)" role="button" aria-controls="gs_ftr_mnu" aria-haspopup="true" class="gs_press gs_md_tb">Help</a></div><div id="gs_ftr_mnu" class="gs_md_d gs_md_ds gs_ttzi gs_md_ulr" role="menu" tabindex="-1"><a role="menuitem" href="/intl/en/scholar/about.html" tabindex="-1" class="gs_md_li">About Scholar</a><a role="menuitem" href="//support.google.com/websearch?p=scholar_dsa&hl=en" tabindex="-1" class="gs_md_li">Search help</a></div></div></div></body></html>