{"title":"采用潜空间平滑法的可信反事实解释方法","authors":"Yan Li;Xia Cai;Chunwei Wu;Xiao Lin;Guitao Cao","doi":"10.1109/TIP.2024.3442614","DOIUrl":null,"url":null,"abstract":"Despite the large-scale adoption of Artificial Intelligence (AI) models in healthcare, there is an urgent need for trustworthy tools to rigorously backtrack the model decisions so that they behave reliably. Counterfactual explanations take a counter-intuitive approach to allow users to explore “what if” scenarios gradually becoming popular in the trustworthy field. However, most previous work on model’s counterfactual explanation cannot generate in-distribution attribution credibly, produces adversarial examples, or fails to give a confidence interval for the explanation. Hence, in this paper, we propose a novel approach that generates counterfactuals in locally smooth directed semantic embedding space, and at the same time gives an uncertainty estimate in the counterfactual generation process. Specifically, we identify low-dimensional directed semantic embedding space based on Principal Component Analysis (PCA) applied in differential generative model. Then, we propose latent space smoothing regularization to rectify counterfactual search within in-distribution, such that visually-imperceptible changes are more robust to adversarial perturbations. Moreover, we put forth an uncertainty estimation framework for evaluating counterfactual uncertainty. Extensive experiments on several challenging realistic Chest X-ray and CelebA datasets show that our approach performs consistently well and better than the existing several state-of-the-art baseline approaches.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Trustworthy Counterfactual Explanation Method With Latent Space Smoothing\",\"authors\":\"Yan Li;Xia Cai;Chunwei Wu;Xiao Lin;Guitao Cao\",\"doi\":\"10.1109/TIP.2024.3442614\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Despite the large-scale adoption of Artificial Intelligence (AI) models in healthcare, there is an urgent need for trustworthy tools to rigorously backtrack the model decisions so that they behave reliably. Counterfactual explanations take a counter-intuitive approach to allow users to explore “what if” scenarios gradually becoming popular in the trustworthy field. However, most previous work on model’s counterfactual explanation cannot generate in-distribution attribution credibly, produces adversarial examples, or fails to give a confidence interval for the explanation. Hence, in this paper, we propose a novel approach that generates counterfactuals in locally smooth directed semantic embedding space, and at the same time gives an uncertainty estimate in the counterfactual generation process. Specifically, we identify low-dimensional directed semantic embedding space based on Principal Component Analysis (PCA) applied in differential generative model. Then, we propose latent space smoothing regularization to rectify counterfactual search within in-distribution, such that visually-imperceptible changes are more robust to adversarial perturbations. Moreover, we put forth an uncertainty estimation framework for evaluating counterfactual uncertainty. Extensive experiments on several challenging realistic Chest X-ray and CelebA datasets show that our approach performs consistently well and better than the existing several state-of-the-art baseline approaches.\",\"PeriodicalId\":94032,\"journal\":{\"name\":\"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10639340/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10639340/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
尽管人工智能(AI)模型在医疗保健领域得到了大规模应用,但仍迫切需要可信的工具来严格回溯模型的决策,使其行为可靠。反事实解释采用反直觉的方法,允许用户探索 "如果 "场景,逐渐在可信领域流行起来。然而,以往大多数关于模型反事实解释的工作都无法可信地生成分布内归因,产生对抗性示例,或无法给出解释的置信区间。因此,在本文中,我们提出了一种在局部平滑的有向语义嵌入空间中生成反事实的新方法,同时给出了反事实生成过程中的不确定性估计。具体来说,我们根据应用于差分生成模型的主成分分析法(PCA)确定低维有向语义嵌入空间。然后,我们提出了潜在空间平滑正则化方法,以纠正分布内的反事实搜索,从而使视觉上可感知的变化对对抗性扰动更加稳健。此外,我们还提出了一个用于评估反事实不确定性的不确定性估计框架。在几个具有挑战性的现实胸部 X 射线和 CelebA 数据集上进行的广泛实验表明,我们的方法始终表现良好,优于现有的几种最先进的基线方法。
A Trustworthy Counterfactual Explanation Method With Latent Space Smoothing
Despite the large-scale adoption of Artificial Intelligence (AI) models in healthcare, there is an urgent need for trustworthy tools to rigorously backtrack the model decisions so that they behave reliably. Counterfactual explanations take a counter-intuitive approach to allow users to explore “what if” scenarios gradually becoming popular in the trustworthy field. However, most previous work on model’s counterfactual explanation cannot generate in-distribution attribution credibly, produces adversarial examples, or fails to give a confidence interval for the explanation. Hence, in this paper, we propose a novel approach that generates counterfactuals in locally smooth directed semantic embedding space, and at the same time gives an uncertainty estimate in the counterfactual generation process. Specifically, we identify low-dimensional directed semantic embedding space based on Principal Component Analysis (PCA) applied in differential generative model. Then, we propose latent space smoothing regularization to rectify counterfactual search within in-distribution, such that visually-imperceptible changes are more robust to adversarial perturbations. Moreover, we put forth an uncertainty estimation framework for evaluating counterfactual uncertainty. Extensive experiments on several challenging realistic Chest X-ray and CelebA datasets show that our approach performs consistently well and better than the existing several state-of-the-art baseline approaches.