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arXiv · 2006.14255

SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization

Haofan Wang·Rakshit Naidu·Joy Michael·Soumya Snigdha Kundu
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Abstract

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.

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Haofan Wang, Rakshit Naidu, Joy Michael, Soumya Snigdha Kundu. 2020-06-25. SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization. https://arxiv.org/abs/2006.14255

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