165 citations · 165 across the 2 of their papers we have counts for
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stat.ML2019
Explanations can be manipulated and geometry is to blame
Ann-Kathrin Dombrowski, Maximilian Alber, Christopher J. Anders +3
Explanation methods aim to make neural networks more trustworthy and interpretable. In this paper, we demonstrate a property of explanation methods which is disconcerting for both…
stat.ML2017★ 165 cited
The (Un)reliability of saliency methods
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo +5
Saliency methods aim to explain the predictions of deep neural networks. These methods lack reliability when the explanation is sensitive to factors that do not contribute to the m…