  Recently, increasing attention has been drawn to the internal mechanisms of
convolutional neural networks, and the reason why the network makes specific
decisions. In this paper, we develop a novel post-hoc visual explanation method
called Score-CAM based on class activation mapping. Unlike previous class
activation mapping based approaches, Score-CAM gets rid of the dependence on
gradients by obtaining the weight of each activation map through its forward
passing score on target class, the final result is obtained by a linear
combination of weights and activation maps. We demonstrate that Score-CAM
achieves better visual performance and fairness for interpreting the decision
making process. Our approach outperforms previous methods on both recognition
and localization tasks, it also passes the sanity check. We also indicate its
application as debugging tools. Official code has been released.
