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Layer-Wise Relevance Propagation: An Overview

  • Grégoire MontavonEmail author
  • Alexander Binder
  • Sebastian Lapuschkin
  • Wojciech Samek
  • Klaus-Robert Müller
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11700)

Abstract

For a machine learning model to generalize well, one needs to ensure that its decisions are supported by meaningful patterns in the input data. A prerequisite is however for the model to be able to explain itself, e.g. by highlighting which input features it uses to support its prediction. Layer-wise Relevance Propagation (LRP) is a technique that brings such explainability and scales to potentially highly complex deep neural networks. It operates by propagating the prediction backward in the neural network, using a set of purposely designed propagation rules. In this chapter, we give a concise introduction to LRP with a discussion of (1) how to implement propagation rules easily and efficiently, (2) how the propagation procedure can be theoretically justified as a ‘deep Taylor decomposition’, (3) how to choose the propagation rules at each layer to deliver high explanation quality, and (4) how LRP can be extended to handle a variety of machine learning scenarios beyond deep neural networks.

Keywords

Explanations Deep Neural Networks Layer-wise Relevance Propagation Deep Taylor Decomposition 

Notes

Acknowledgements

This work was supported by the German Ministry for Education and Research as Berlin Big Data Centre (01IS14013A), Berlin Center for Machine Learning (01IS18037I) and TraMeExCo (01IS18056A). Partial funding by DFG is acknowledged (EXC 2046/1, project-ID: 390685689). This work was also supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451, No. 2017-0-01779).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Grégoire Montavon
    • 1
    Email author
  • Alexander Binder
    • 2
  • Sebastian Lapuschkin
    • 3
  • Wojciech Samek
    • 3
  • Klaus-Robert Müller
    • 1
    • 4
    • 5
  1. 1.Technische Universität BerlinBerlinGermany
  2. 2.Singapore University of Technology and DesignSingaporeSingapore
  3. 3.Fraunhofer Heinrich Hertz InstituteBerlinGermany
  4. 4.Korea UniversitySeongbuk-gu, SeoulKorea
  5. 5.Max Planck Institute for InformaticsSaarbrückenGermany

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