{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/network-dissection-quantifying","title":"Network Dissection: Quantifying Interpretability of Deep Visual Representations","arxiv_id":"1704.05796","date":"2017-04-19","proceeding":"CVPR 2017 7","authors":["David Bau","Bolei Zhou","Aditya Khosla","Aude Oliva","Antonio Torralba"],"abstract":"We propose a general framework called Network Dissection for quantifying the\ninterpretability of latent representations of CNNs by evaluating the alignment\nbetween individual hidden units and a set of semantic concepts. Given any CNN\nmodel, the proposed method draws on a broad data set of visual concepts to\nscore the semantics of hidden units at each intermediate convolutional layer.\nThe units with semantics are given labels across a range of objects, parts,\nscenes, textures, materials, and colors. We use the proposed method to test the\nhypothesis that interpretability of units is equivalent to random linear\ncombinations of units, then we apply our method to compare the latent\nrepresentations of various networks when trained to solve different supervised\nand self-supervised training tasks. We further analyze the effect of training\niterations, compare networks trained with different initializations, examine\nthe impact of network depth and width, and measure the effect of dropout and\nbatch normalization on the interpretability of deep visual representations. We\ndemonstrate that the proposed method can shed light on characteristics of CNN\nmodels and training methods that go beyond measurements of their discriminative\npower.","url_abs":"http://arxiv.org/abs/1704.05796v1","url_pdf":"http://arxiv.org/pdf/1704.05796v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"network-dissection-quantifying","repo_url":"https://github.com/csailvision/netdissect-lite","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1704.05796","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}