{"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/interpretability-beyond-feature-attribution","title":"Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)","arxiv_id":"1711.11279","date":"2017-11-30","proceeding":"ICML 2018 7","authors":["Been Kim","Martin Wattenberg","Justin Gilmer","Carrie Cai","James Wexler","Fernanda Viegas","Rory Sayres"],"abstract":"The interpretation of deep learning models is a challenge due to their size,\ncomplexity, and often opaque internal state. In addition, many systems, such as\nimage classifiers, operate on low-level features rather than high-level\nconcepts. To address these challenges, we introduce Concept Activation Vectors\n(CAVs), which provide an interpretation of a neural net's internal state in\nterms of human-friendly concepts. The key idea is to view the high-dimensional\ninternal state of a neural net as an aid, not an obstacle. We show how to use\nCAVs as part of a technique, Testing with CAVs (TCAV), that uses directional\nderivatives to quantify the degree to which a user-defined concept is important\nto a classification result--for example, how sensitive a prediction of \"zebra\"\nis to the presence of stripes. Using the domain of image classification as a\ntesting ground, we describe how CAVs may be used to explore hypotheses and\ngenerate insights for a standard image classification network as well as a\nmedical application.","url_abs":"http://arxiv.org/abs/1711.11279v5","url_pdf":"http://arxiv.org/pdf/1711.11279v5.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":"interpretability-beyond-feature-attribution","repo_url":"https://github.com/tensorflow/tcav","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"interpretability-beyond-feature-attribution","repo_url":"https://github.com/giovannimaffei/concept_activation_vectors","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"interpretability-beyond-feature-attribution","repo_url":"https://github.com/google-research/mood-board-search","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"interpretability-beyond-feature-attribution","repo_url":"https://github.com/jwendyr/tcav","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"interpretability-beyond-feature-attribution","repo_url":"https://github.com/maragraziani/iMIMIC-RCVs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"interpretability-beyond-feature-attribution","repo_url":"https://github.com/mbakler/Tcav_pytorch_implementation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"interpretability-beyond-feature-attribution","repo_url":"https://github.com/medgift/iMIMIC-RCVs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"interpretability-beyond-feature-attribution","repo_url":"https://github.com/pnxenopoulos/cav-keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"interpretability-beyond-feature-attribution","repo_url":"https://github.com/soumyadip1995/TCAV","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"interpretability-beyond-feature-attribution","repo_url":"https://github.com/fursovia/tcav_nlp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"interpretability-beyond-feature-attribution","repo_url":"https://github.com/pytorch/captum","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.11279","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.11279"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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