Recipro-CAM: Gradient-free reciprocal class activation map
- URL: http://arxiv.org/abs/2209.14074v1
- Date: Wed, 28 Sep 2022 13:15:03 GMT
- Title: Recipro-CAM: Gradient-free reciprocal class activation map
- Authors: Seok-Yong Byun, Wonju Lee
- Abstract summary: We propose a lightweight architecture and gradient free Reciprocal CAM (Recipro-CAM) 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 addition, Recipro-CAM exhibits a saliency map generation rate similar to Grad-CAM and approximately 148 times faster than Score-CAM.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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.
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