Abstract
Convolutional neural network (CNN) becomes one of the most popular and prominent deep learning architectures for computer vision, but its black box feature hides the internal prediction process. For this reason, AI practitioners have shed light on explainable AI to provide the interpretability of the model behavior. In particular, class activation map (CAM) and Grad-CAM based methods have shown promise results, but they have architectural limitation or gradient computing burden. To resolve these, Score-CAM has been suggested as a gradient-free method, however, it requires more execution time compared to CAM or Grad-CAM based methods. Therefore, we propose a lightweight architecture and gradient free Reciprocal CAM (Recipro-CAM) by spatially masking the extracted feature maps to exploit the correlation between activation maps and network outputs. With the proposed method, we achieved the gains of 1:78 - 3:72% in the ResNet family compared to Score-CAM in Average Drop- Coherence-Complexity (ADCC) metric, excluding the VGG-16 (1:39% drop). In addition, Recipro-CAM exhibits a saliency map generation rate similar to Grad-CAM and approximately 148 times faster than Score-CAM.
Abstract (translated)
URL
https://arxiv.org/abs/2209.14074
