Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs

Understand

To have a better understanding and usage of Convolution Neural Networks (CNNs), the visualization and interpretation of CNNs has attracted increasing attention in recent years.

• In particular, several Class Activation Mapping (CAM) methods have been proposed to discover the connection between CNN's decision and image regions.
• In spite of the reasonable visualization, lack of clear and sufficient theoretical support is the main limitation of these methods.
• In this paper, we introduce two axioms -- Conservation and Sensitivity -- to the visualization paradigm of the CAM methods.