Relevance-CAM: Your Model Already Knows Where to Look

Abstract

With increasing fields of application for neural networks and the development of neural networks, the ability to explain deep learning models is also becoming increasingly important. Especially, prior to practical applications, it is crucial to analyze a model’s inference and the process of generating the results. A common explanation method is Class Activation Mapping(CAM) based method where it is often used to understand the last layer of the convolutional neural networks popular in the field of Computer Vision. In this paper, we propose a novel CAM method named Relevance-weighted Class Activation Mapping(Relevance-CAM) that utilizes Layer-wise Relevance Propagation to obtain the weighting components. This allows the explanation map to be faithful and robust to the shattered gradient problem, a shared problem of the gradient based CAM methods that causes noisy saliency maps for intermediate layers. Therefore, our proposed method can better explain a model by correctly analyzing the intermediate layers as well as the last convolutional layer. In this paper, we visualize how each layer of the popular image processing models extracts class specific features using Relevance-CAM, evaluate the localization ability, and show why the gradient based CAM cannot be used to explain the intermediate layers, proven by experimenting the weighting component. Relevance-CAM outperforms other CAM-based methods in recognition and localization evaluation in layers of any depth. The source code is available at: https://github.com/mongeoroo/Relevance-CAM