arXiv Machine Learning September 21, 2026 By Dane Williamson, Yangfeng Ji, Matthew Dwyer

CASE: Contrastive Activation for Class-Sensitive Explanations

Read the original on arXiv Machine Learning →

The paper introduces a diagnostic test for class sensitivity in saliency methods, revealing that many popular techniques produce nearly identical explanations regardless of the predicted class. This limitation appears across different architectures and datasets, indicating a structural issue. To address this, the authors propose CASE, a contrastive explanation method that isolates features uniquely discriminative for the predicted class, and demonstrate its improved fidelity and class specificity through experiments.

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