Explanations for Occluded Images

Hana Chockler, Daniel Kroening, Youcheng Sun

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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Abstract

Existing algorithms for explaining the output of image classifiers perform poorly on inputs where the object ofinterest is partially occluded. We present a novel, black-box algorithm for computing explanations that uses a principled approach based on causal theory. We have implemented the method in the DEEPCOVER tool. We obtain explanations that are much more accurate than those generated by the existing explanation tools on images with occlusions and observe a level of performance comparable to the state of the art when explaining images without occlusions.
Original languageEnglish
Title of host publicationProceedings of International Conference on Computer Vision (ICCV)
Publication statusAccepted/In press - Oct 2021

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