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Gradient-Based Vs. Propagation-Based Explanations: An Axiomatic Comparison

  • Grégoire MontavonEmail author
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11700)

Abstract

Deep neural networks, once considered to be inscrutable black-boxes, are now supplemented with techniques that can explain how these models decide. This raises the question whether the produced explanations are reliable. In this chapter, we consider two popular explanation techniques, one based on gradient computation and one based on a propagation mechanism. We evaluate them using three “axiomatic” properties: conservation, continuity, and implementation invariance. These properties are tested on the overall explanation, but also at intermediate layers, where our analysis brings further insights on how the explanation is being formed.

Keywords

Explanations Deep neural networks Axioms 

Notes

Acknowledgements

This work was supported by the German Ministry for Education and Research as Berlin Center for Machine Learning (01IS18037I). Partial funding by DFG is acknowledged (EXC 2046/1, project-ID: 390685689). The author is grateful to Klaus-Robert Müller for the valuable feedback.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Technische Universität BerlinBerlinGermany

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