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Sanity Checks for Saliency Maps
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| cascading randomization of model weights | Gradient saliency · Inception v3 ImageNet, CNN/MLP MNIST and Fashion MNIST | Spearman rank correlation with original map | Hurt |
| cascading randomization of model weights | Guided BackProp and Guided GradCAM · Inception v3 ImageNet, MNIST, Fashion MNIST | similarity to original map | No clear effect |
| cascading randomization of model weights | GradCAM · Inception v3 ImageNet | similarity to original map | Hurt |
| cascading randomization of model weights | Integrated Gradients and Gradient x Input · Inception v3 ImageNet | Spearman rank correlation (abs vs no abs) | Mixed |
| train on randomly permuted labels | Gradient and SmoothGrad · MNIST, Fashion MNIST | rank correlation with true-label model map | Hurt |
| train on randomly permuted labels | Guided BackProp · MNIST | visual plausibility of map | No clear effect |
| independent single-layer randomization | Guided BackProp and Guided GradCAM · Inception v3, CNN, MLP | similarity to original map | No clear effect |
Closest papers by meaning
- Rethinking Interpretation: Input-Agnostic Saliency Mapping of Deep Visual Classifiers (AAAI 2023 · similarity 0.91)
- Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability (NeurIPS 2022 · similarity 0.91)
- Sanity Checks for Saliency Metrics (AAAI 2020 · similarity 0.90)
- New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound (NeurIPS 2022 · similarity 0.90)
- Explainable Saliency: Articulating Reasoning with Contextual Prioritization (CVPR 2025 · similarity 0.87)
- DiffCAM: Data-Driven Saliency Maps by Capturing Feature Differences (CVPR 2025 · similarity 0.86)
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