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As the request for deep learning solutions increases, the need forexplainability is even more fundamental. In this setting, particular attentionhas been given to visualization techniques, that try to attribute the rightrelevance to each input pixel with respect to the output of the network. Inthis paper, we focus on Class Activation Mapping (CAM) approaches, whichprovide an effective visualization by taking weighted averages of theactivation maps. To enhance the evaluation and the reproducibility of suchapproaches, we propose a novel set of metrics to quantify explanation maps,which show better effectiveness and simplify comparisons between approaches. Toevaluate the appropriateness of the proposal, we compare different CAM-basedvisualization methods on the entire ImageNet validation set, fostering propercomparisons and reproducibility.

Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis