Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations
Attribution methods are widely used to characterize the evidence underlying model predictions, yet their potential to improve model behavior remains underexplored. Attribution inconsistency under label-preserving geometric transformations may indicate transformation-sensitive evidence reliance, motivating attribution regularization. However, such supervision is valid only when attribution faithfully reflects the evidence driving predictions. Existing self-supervised methods typically align gradient-based maps such as Grad-CAM, whose limited faithfulness means that attribution consistency need not imply consistency of the underlying decision process, leaving transformation robustness unres...
| Authors | Xianghao Jiao, Ruoyu Chen, Wei Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 July 2026 |
| Last updated at source | 26 July 2026 |
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