Color, Edge, and Pixel-wise Explanation of Predictions Based on Interpretable Neural Network Model

Jay Hoon Jung, YoungMin Kwon

Abstract: We design an interpretable network model by introducing explainable components into a Deep Neural Network (DNN). We substituted the first kernels of a Convolutional Neural Network (CNN) and a ResNet-50 with the well-known edge detecting filters such as Sobel, Prewitt, and other filters. Each filters' relative importance scores are measured with a variant of Layer-wise Relevance Propagation (LRP) method proposed by [1]. Since the effects of the edge detecting filters are well understood, our model provides three different scores to explain individual predictions: the scores with respect to (1) colors, (2) edge filters, and (3) pixels of the image. Our method provides more tools to analyze the predictions by highlighting the location of important edges and colors in the images. Furthermore, the general features of a category can be shown in our scores as well as individual predictions. At the same time, the model does not degrade performances on MNIST, Fruit360 and ImageNet datasets.