Diffusion Models for Counterfactual Explanations

Abstract

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.