用于反事实解释的扩散模型

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Guillaume Jeanneret, Loïc Simon, Frédéric Jurie
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引用次数: 0

摘要

反事实解释作为一种事后框架,在提高图像分类器的解释能力方面取得了可喜的成果。本文提出的 DiME 是一种利用最新扩散模型生成反事实图像的方法。该方法使用引导生成扩散过程,利用目标分类器的梯度生成输入实例的反事实解释。此外,我们还研究了评估虚假相关性的现有策略,并提出了一种新的评估方法--相关性差异,它能更有效地检测出此类相关性,从而扩展了评估方法。所提供的工作包括全面的消融研究和彻底的实验验证,证明所提出的算法在 CelebA、CelebAHQ 和 BDD100k 数据集上的表现优于之前最先进的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Diffusion Models for Counterfactual Explanations
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.
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来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
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
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