  Although input-gradients techniques have evolved to mitigate and tackle the
challenges associated with gradients, modern gradient-weighted CAM approaches
still rely on vanilla gradients, which are inherently susceptible to the
saturation phenomena. Despite recent enhancements have incorporated
counterfactual gradient strategies as a mitigating measure, these local
explanation techniques still exhibit a lack of sensitivity to their baseline
parameter. Our work proposes a gradient-weighted CAM augmentation that tackles
both the saturation and sensitivity problem by reshaping the gradient
computation, incorporating two well-established and provably approaches:
Expected Gradients and kernel smoothing. By revisiting the original formulation
as the smoothed expectation of the perturbed integrated gradients, one can
concurrently construct more faithful, localized and robust explanations which
minimize infidelity. Through fine modulation of the perturbation distribution
it is possible to regulate the complexity characteristic of the explanation,
selectively discriminating stable features. Our technique, Expected Grad-CAM,
differently from recent works, exclusively optimizes the gradient computation,
purposefully designed as an enhanced substitute of the foundational Grad-CAM
algorithm and any method built therefrom. Quantitative and qualitative
evaluations have been conducted to assess the effectiveness of our method.
