  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.
