A Benchmark for Interpretability Methods in Deep Neural Networks

NeurIPS 2019 Sara HookerDumitru ErhanPieter-Jan KindermansBeen Kim

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... (read more)

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