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IEEE/ICACT20240312 Question.1 Questioner: sikandarshigri77@gmail.com 2024-02-05 오후 9:45:15	IEEE/ICACT20240312 Answer.1 Answer by Auhor rrmurali.199@gmail.com 2024-02-05 오후 9:45:15
You have used Eigen CAM for explainability. Did you try GradCAM as well? Is there any specific reason for using Eigen CAM?	Thanks for your valuable question. Though both Eigen CAM and GradCAM are popular techniques for visualizing and interpreting CNNs by highlighting important regions of an image, Eigen CAM emphasizes the importance of eigenvalues in the feature maps of CNNs, providing insights into how different parts of the image contribute to the final classification decision. And the GradCAM, on the other hand, computes the gradient of the target class with respect to the final convolutional layer to highlight important regions. As we tried both CAM techniques, the final choice was made based on the specific requirements of our main task which is to detect the rip currents, and the architecture of the model we used YOLO v8. Also Eigen CAM showed the superiority in terms of performance, ease of implementation, and primarily the compatibility with the architecture of the YOLO v8 model.

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