Shap-CAM: Visual Explanations for Convolutional Neural Networks based on Shapley Value. (arXiv:2208.03608v2 [cs.CV] UPDATED)

Explaining deep convolutional neural networks has been recently drawing
increasing attention since it helps to understand the networks' internal
operations and why they make certain decisions. Saliency maps, which emphasize
salient regions largely connected to the network's decision-making, are one of
the most common ways for visualizing and analyzing deep networks in the
computer vision community. However, saliency maps generated by existing methods
cannot represent authentic information in images due to the unproven proposals
about the weights of activation maps …

arxiv convolutional neural networks cv networks neural networks shap value