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Improving Explanations of Image Classification with Ensembles of Learners

Authors

Aadil Ahamed1, Kamran Alipour1, Sateesh Kumar1, Severine Soltani1 and Michael Pazzani2, 1University of California, USA, 2Information Sciences Institute, USA

Abstract

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. Recent research has shown that many common XAI methods do not accurately identify the regions that human experts consider important. We propose 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. Furthermore, we show that human experts prefer the explanations produced by ensembles to those of individual networks.

Keywords

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

Full Text  Volume 12, Number 18