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arXiv · 1711.06104

Towards better understanding of gradient-based attribution methods for Deep Neural Networks

Marco Ancona·Enea Ceolini·Cengiz Öztireli·Markus Gross
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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.

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Marco Ancona, Enea Ceolini, Cengiz Öztireli, Markus Gross. 2018-03-07. Towards better understanding of gradient-based attribution methods for Deep Neural Networks. https://arxiv.org/abs/1711.06104

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