Volume 13, Number 6

Improving Explanations of Image Classifiers: Ensembles and Multitask Learning


Michael Pazzani1, Severine Soltani2, Sateesh Kumar2, Kamran Alipour2 and Aadil Ahamed2, 1Information Sciences Institute, USA, 2University of California, USA


In explainable AI (XAI) for deep learning, saliency maps, heatmaps, or attention maps are commonly used to identify important regions for the classification of images of explanations. We address two important limitations of heatmaps. First, they do not correspond to type of explanations typically produced by human experts. Second, recent research has shown that many common XAI methods do not accurately identify the regions that human experts consider important. We propose using multitask learning to identify diagnostic features in images and averaging explanations from ensembles of learners to increase the accuracy of explanations. Our technique is general and can be used with multiple deep learning architectures and multiple XAI algorithms. We show that this method decreases the difference between regions of interest of XAI algorithms and those identified by human experts and the multitask learning supports the type of explanations produced by human experts. Furthermore, we show that human experts prefer the explanations produced by ensembles to those of individual networks.


Neural Networks, Machine Learning, Explainable AI, Image Classification, Computer Vision.