Layer-wise relevance propagation for deep neural network architectures

Alexander Binder, Sebastian Bach, Gregoire Montavon, Klaus Muller, Wojciech Samek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

28 Citations (Scopus)

Abstract

We present the application of layer-wise relevance propagation to several deep neural networks such as the BVLC reference neural net and googlenet trained on ImageNet and MIT Places datasets. Layer-wise relevance propagation is a method to compute scores for image pixels and image regions denoting the impact of the particular image region on the prediction of the classifier for one particular test image. We demonstrate the impact of different parameter settings on the resulting explanation.

Original languageEnglish
Title of host publicationLecture Notes in Electrical Engineering
PublisherSpringer Verlag
Pages913-922
Number of pages10
Volume376
ISBN (Print)9789811005565
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventInternational Conference on Information Science and Applications, ICISA 2016 - Minh City, Viet Nam
Duration: 2016 Feb 152016 Feb 18

Publication series

NameLecture Notes in Electrical Engineering
Volume376
ISSN (Print)18761100
ISSN (Electronic)18761119

Other

OtherInternational Conference on Information Science and Applications, ICISA 2016
CountryViet Nam
CityMinh City
Period16/2/1516/2/18

Keywords

  • Deep neural networks
  • Non-linear explanations

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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  • Cite this

    Binder, A., Bach, S., Montavon, G., Muller, K., & Samek, W. (2016). Layer-wise relevance propagation for deep neural network architectures. In Lecture Notes in Electrical Engineering (Vol. 376, pp. 913-922). (Lecture Notes in Electrical Engineering; Vol. 376). Springer Verlag. https://doi.org/10.1007/978-981-10-0557-2_87