Augmented Score-CAM: High resolution visual interpretations for deep neural networks

摘要

There is an increasing demand to understand how neural networks make decisions when classifying images. Recent deep learning models have a black box architecture that limits the ability to explore their functionality. This paper presents an explainable method called Augmented Score-CAM, built on top of the existing Score-CAM and the existing image augmentation techniques. Unlike previous methods that relied on one input image to generate class activation maps, we adopt the image augmentation technique used to train convolutional neural networks. We use the input image to create a set of augmented images and generate a class activation map for each. The final activation map is obtained by combining augmented activation maps. We evaluate the model by performing qualitative and quantitative experiments. Augmented Score-CAM outperformed Score-CAM in terms of human trust, faithfulness, and object localization. Our model passed the sanity check and was found to be sensitive to network and dataset randomization. Moreover, we proposed the use of feature space augmentations by embedding neural style transfer in the model.