Cascading Class Activation Mapping: A Counterfactual Reasoning-Based Explainable Method for Comprehensive Feature Discovery

Most Convolutional Neural Network (CNN) interpretation techniques visualize only the dominant cues that the model relies on, but there is no guarantee that these represent all the evidence the model uses for classification. This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered. This study introduces CasCAM (Cascaded Class Activation Mapping) to address this fundamental limitation through counterfactual reasoning. By asking “if this dominant cue were absent, what other evidence would the model use?”, CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them. Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods.