Generating self-attention activation maps for visual interpretations of convolutional neural networks

Abstract

In recent years, many interpretable methods based on class activation maps (CAMs) have served as an important judging basis for the predictions of convolutional neural networks (CNNs). However, these methods still suffer from the problems of gradient noise, weight distortion, and perturbation deviation. In this work, we present self-attention class activation map (SA-CAM) and shed light on how it uses the self-attention mechanism to refine the existing CAM methods. In addition to generating basic activation feature maps, SA-CAM adds an attention skip connection as a regularization item for each feature map which further refines the focus area of an underlying CNN model. By introducing an attention branch and constructing a new attention operator, SA-CAM greatly alleviates the limitations of the CAM methods. The experimental results on the ImageNet dataset show that SA-CAM can not only generate highly accurate and intuitive interpretation but also have robust stability in adversarial comparison with the state-of-the-art CAM methods.