Counterfactual Contrastive Analysis
Visual Counterfactual Explanations (VCEs) aim to explain image classifiers by generating minimally edited and realistic versions of an input image that change the classifier's prediction. Existing VCE methods are inherently classifier-dependent and therefore susceptible to classifier biases and failure modes, such as sensitivity to shortcut features and calibration errors. In this paper, we propose a classifier-free approach for visual counterfactual generation based on Contrastive Analysis (CA). Given two datasets corresponding to different classes (e.g., healthy and patients), we disentangle the generative factors that are common across the two datasets from those that are salient to ea...
| Authors | Yunlong He, Pietro Gori |
|---|---|
| Primary category | cs.CV |
| Published | 19 August 2026 |
| Last updated at source | 3 September 2026 |
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