Compositional Explanations for Image Classifiers

Existing algorithms for explaining the output of image classifiers perform poorly on inputs where the object of interest is partially occluded. We present a novel, black-box algorithm for computing explanations that uses a principled approach based on causal theory. We implement the method in the tool CET (Compositional Explanation Tool). Owing to the compositionality in its algorithm, CET computes explanations that are much more accurate than those generated by the existing explanation tools on images with occlusions and delivers a level of performance comparable to the state of the art when explaining images without occlusions.