Learning Quantifiable Visual Explanations Without Ground-Truth

In three linesNew metric to evaluate XAI methods without ground-truth, based on continuous input perturbation. Measures sufficiency and necessity of attributed information. Also proposes trainable XAI method as adapter on black-box models, generating causal explanations without degrading performance.

Summary generated by Claude — human-verified