A perturbation-based metric using FGSM flips of ±1/255 instead of zero-masking gives more consistent and monotonic evaluation of attribution maps across 15 CNN-dataset pairs, with SmoothGrad ranked first.
Challenging the black box: A comprehensive evaluation of attribution maps of cnn applications in agriculture and forestry, 2024
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Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach
A perturbation-based metric using FGSM flips of ±1/255 instead of zero-masking gives more consistent and monotonic evaluation of attribution maps across 15 CNN-dataset pairs, with SmoothGrad ranked first.