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Learning Conformal Explainers for Image Classifiers
Amr Alkhatib, Stephanie Lowry
https://arxiv.org/abs/2509.21209 https://arxiv.org/pdf/2509.21209

Learning Conformal Explainers for Image Classifiers
Feature attribution methods are widely used for explaining image-based predictions, as they provide feature-level insights that can be intuitively visualized. However, such explanations often vary in their robustness and may fail to faithfully reflect the reasoning of the underlying black-box model. To address these limitations, we propose a novel conformal prediction-based approach that enables users to directly control the fidelity of the generated explanations. The method identifies a subset…

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