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.
Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis
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abstract
As the request for deep learning solutions increases, the need for explainability is even more fundamental. In this setting, particular attention has been given to visualization techniques, that try to attribute the right relevance to each input pixel with respect to the output of the network. In this paper, we focus on Class Activation Mapping (CAM) approaches, which provide an effective visualization by taking weighted averages of the activation maps. To enhance the evaluation and the reproducibility of such approaches, we propose a novel set of metrics to quantify explanation maps, which show better effectiveness and simplify comparisons between approaches. To evaluate the appropriateness of the proposal, we compare different CAM-based visualization methods on the entire ImageNet validation set, fostering proper comparisons and reproducibility.
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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.