Interpretation of the underlying mechanisms of Deep Convolutional Neural Networks has become an important aspect of research in the field of deep learning due to their applications in high-risk environments. To explain these black-box architectures there have been many methods applied so the internal decisions can be analyzed and understood. In this paper, built on the top of Score-CAM, we introduce an enhanced visual explanation in terms of visual sharpness called SS-CAM, which produces centralized localization of object features within an image through a smooth operation. We evaluate our method on the ILSVRC 2012 Validation dataset, which outperforms Score-CAM on both faithfulness and localization tasks.
@article{arxiv.2006.14255,
title = {SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization},
author = {Haofan Wang and Rakshit Naidu and Joy Michael and Soumya Snigdha Kundu},
journal= {arXiv preprint arXiv:2006.14255},
year = {2020}
}