C3-LRP: Visual Explanation Generation based on Layer-Wise Relevance Propagation for ResNet

Abstract

In this paper, we focus on the task of visualizing important regions in an image as high-quality visual explanations of the model’s decisions with a clear theoretical background. We introduce a novel calculation method for Layer-wise Relevance Propagation (LRP) specifically tailored to models featuring skip connections such as ResNet. This method’s strength lies in its adaptability, as the backpropagation technique is distinctly defined for each layer, enhancing its extensibility. To validate our method, we conduct an experiment on the CUB-200-2011 dataset. The proposed method successfully generates appropriate explanations and, based on the Insertion-Deletion score, outperforms the baseline methods.