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Towards better understanding of gradient-based attribution methods for Deep Neural Networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, Markus Gross
Feb 15, 2018 (modified: Mar 07, 2018)ICLR 2018 Conference Blind Submissionreaders: everyoneShow Bibtex
Abstract:Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that has gain increasing attention over the last few years. While several methods have been proposed to explain network predictions, there have been only a few attempts to compare them from a theoretical perspective. What is more, no exhaustive empirical comparison has been performed in the past. In this work we analyze four gradient-based attribution methods and formally prove conditions of equivalence and approximation between them. By reformulating two of these methods, we construct a unified framework which enables a direct comparison, as well as an easier implementation. Finally, we propose a novel evaluation metric, called Sensitivity-n and test the gradient-based attribution methods alongside with a simple perturbation-based attribution method on several datasets in the domains of image and text classification, using various network architectures.
TL;DR:Four existing backpropagation-based attribution methods are fundamentally similar. How to assess it?
Keywords:Deep Neural Networks, Attribution methods, Theory of deep learning
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