SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization
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Deep Convolution Neural Networks are often referred to as black-box models due to minimal understandings of their internal actions. As an effort to develop more complex explainable deep learning models, many methods have been proposed to reveal the internal mechanism of the decisionmaking process. 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 sharper localizations of object features within an image by smoothing. We evaluate our method on three well-known datasets: ILSVRC 2012, Stanford40 and PASCAL VOC 2007 dataset. Our approach outperforms when evaluated on both fairness and localization tasks.
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