A Benchmark for Interpretability Methods in Deep Neural Networks

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Authors Sara Hooker, Pieter-Jan Kindermans, Dumitru Erhan, Been Kim
Journal/Conference Name NeurIPS 2019 12
Paper Category
Paper Abstract We propose an empirical measure of the approximate accuracy of feature importance estimates in deep neural networks. Our results across several large-scale image classification datasets show that many popular interpretability methods produce estimates of feature importance that are not better than a random designation of feature importance. Only certain ensemble based approaches---VarGrad and SmoothGrad-Squared---outperform such a random assignment of importance. The manner of ensembling remains critical, we show that some approaches do no better then the underlying method but carry a far higher computational burden.
Date of publication 2018
Code Programming Language Multiple
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