{"title":"用于反事实解释的扩散模型","authors":"Guillaume Jeanneret, Loïc Simon, Frédéric Jurie","doi":"10.1016/j.cviu.2024.104207","DOIUrl":null,"url":null,"abstract":"<div><div>Counterfactual explanations have demonstrated promising results as a post-hoc framework to improve the explanatory power of image classifiers. Herein, this paper proposes DiME, a method that allows the generation of counterfactual images using the latest diffusion models. The proposed method uses a guided generative diffusion process to exploit the gradients of the target classifier to generate counterfactual explanations of the input instances. Furthermore, we examine present strategies for assessing spurious correlations and expand the assessment methods by presenting a novel measure, Correlation Difference, which is more efficient at detecting such correlations. The provided work includes a comprehensive ablation study and a thorough experimental validation demonstrating that the proposed algorithm outperforms previous state-of-the-art results on the CelebA, CelebAHQ and BDD100k datasets.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Diffusion Models for Counterfactual Explanations\",\"authors\":\"Guillaume Jeanneret, Loïc Simon, Frédéric Jurie\",\"doi\":\"10.1016/j.cviu.2024.104207\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Counterfactual explanations have demonstrated promising results as a post-hoc framework to improve the explanatory power of image classifiers. Herein, this paper proposes DiME, a method that allows the generation of counterfactual images using the latest diffusion models. The proposed method uses a guided generative diffusion process to exploit the gradients of the target classifier to generate counterfactual explanations of the input instances. Furthermore, we examine present strategies for assessing spurious correlations and expand the assessment methods by presenting a novel measure, Correlation Difference, which is more efficient at detecting such correlations. The provided work includes a comprehensive ablation study and a thorough experimental validation demonstrating that the proposed algorithm outperforms previous state-of-the-art results on the CelebA, CelebAHQ and BDD100k datasets.</div></div>\",\"PeriodicalId\":50633,\"journal\":{\"name\":\"Computer Vision and Image Understanding\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Vision and Image Understanding\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1077314224002881\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Vision and Image Understanding","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1077314224002881","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Counterfactual explanations have demonstrated promising results as a post-hoc framework to improve the explanatory power of image classifiers. Herein, this paper proposes DiME, a method that allows the generation of counterfactual images using the latest diffusion models. The proposed method uses a guided generative diffusion process to exploit the gradients of the target classifier to generate counterfactual explanations of the input instances. Furthermore, we examine present strategies for assessing spurious correlations and expand the assessment methods by presenting a novel measure, Correlation Difference, which is more efficient at detecting such correlations. The provided work includes a comprehensive ablation study and a thorough experimental validation demonstrating that the proposed algorithm outperforms previous state-of-the-art results on the CelebA, CelebAHQ and BDD100k datasets.
期刊介绍:
The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views.
Research Areas Include:
• Theory
• Early vision
• Data structures and representations
• Shape
• Range
• Motion
• Matching and recognition
• Architecture and languages
• Vision systems