How to Evaluate and Refine your CAM
Class attribution maps (CAMs) provide local explanations for the decisions of convolutional neural networks. While widely used in practice, the evaluation of CAMs remains challenging due to the lack of ground-truth explanations, making it difficult to evaluate the soundness of existing metrics. Independently, most commonly used CAM methods produce low-resolution attribution maps, which limits their usefulness for detailed interpretability. To address the evaluation challenge, we introduce a synthetic dataset with ground-truth attributions that enables a rigorous comparison of CAM evaluation metrics. Using this dataset, we analyze existing metrics and propose ARCC, a new composite metric t...
| Authors | Luca Domeniconi, Alessandra Stramiglio, Michele Lombardi et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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