Visualizing Color-wise Saliency of Black-Box Image Classification Models

Yuhki Hatakeyama, Hiroki Sakuma, Yoshinori Konishi, Kohei Suenaga; Proceedings of the Asian Conference on Computer Vision (ACCV), 2020

Image classification based on machine learning is being commonly used.However, a classification result given by an advanced method, including deep learning, is often hard to interpret.This problem of interpretability is one of the major obstacles in deploying a trained model in safety-critical systems.Several techniques have been proposed to address this problem;one of which is RISE, which explains a classification result by a heatmap, called a saliency map, that explains the significance of each pixel.We propose MC-RISE (Multi-Color RISE), which is an enhancement of RISE to take color information into account in an explanation.Our method not only shows the saliency of each pixel in a given image as the original RISE does, but the significance of color components of each pixel;a saliency map with color information is useful especially in the domain where the color information matters (e.g., traffic-sign recognition).We implemented MC-RISE and evaluate them using two datasets (GTSRB and ImageNet) to demonstrate the effectiveness of our methods in comparison with existing techniques for interpreting image classification results.