{"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/www-a-unified-framework-for-explaining-what-1","title":"WWW: A Unified Framework for Explaining What Where and Why of Neural Networks by Interpretation of Neuron Concepts","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Yong Hyun Ahn","Hyeon Bae Kim","Seong Tae Kim"],"abstract":"    Recent advancements in neural networks have showcased their remarkable capabilities across various domains. Despite these successes the \"black box\" problem still remains. To address this we propose a novel framework WWW that offers the 'what' 'where' and 'why' of the neural network decisions in human-understandable terms. Specifically WWW utilizes an adaptive selection for concept discovery employing adaptive cosine similarity and thresholding techniques to effectively explain 'what'. To address the 'where' and 'why' we proposed a novel combination of neuron activation maps (NAMs) with Shapley values generating localized concept maps and heatmaps for individual inputs. Furthermore WWW introduces a method for predicting uncertainty leveraging heatmap similarities to estimate the prediction's reliability. Experimental evaluations of WWW demonstrate superior performance in both quantitative and qualitative metrics outperforming existing methods in interpretability. WWW provides a unified solution for explaining 'what' 'where' and 'why' introducing a method for localized explanations from global interpretations and offering a plug-and-play solution adaptable to various architectures. The code is available at: https://github.com/ailab-kyunghee/WWW    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Ahn_WWW_A_Unified_Framework_for_Explaining_What_Where_and_Why_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Ahn_WWW_A_Unified_Framework_for_Explaining_What_Where_and_Why_CVPR_2024_paper.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":"www-a-unified-framework-for-explaining-what-1","repo_url":"https://github.com/ailab-kyunghee/www","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"heatmap","method_name":"Heatmap"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}