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arXiv AI By Yunlong He, Pietro Gori

Counterfactual Contrastive Analysis

Read the original on arXiv AI →

The paper introduces a classifier‑free method for generating visual counterfactual explanations (VCEs) using Contrastive Analysis (CA). By separating generative factors common to two datasets from those specific to each class, the approach swaps only the salient factors to produce counterfactual images, thereby avoiding reliance on classifier decision boundaries. Leveraging StyleGAN2’s high‑quality synthesis and a feature‑space latent representation, the method supports multiple salient factors per dataset and achieves superior counterfactual quality on three medical imaging datasets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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