Rethinking gradient weights' influence over saliency map estimation. (arXiv:2207.05374v1 [cs.CV])

Class activation map (CAM) helps to formulate saliency maps that aid in
interpreting the deep neural network's prediction. Gradient-based methods are
generally faster than other branches of vision interpretability and independent
of human guidance. The performance of CAM-like studies depends on the governing
model's layer response, and the influences of the gradients. Typical
gradient-oriented CAM studies rely on weighted aggregation for saliency map
estimation by projecting the gradient maps into single weight values, which may
lead to over generalized saliency …

arxiv cv gradient influence map