Layer-Wise Relevance Propagation for Deep Neural Network Architectures

  • Alexander Binder
  • Sebastian Bach
  • Gregoire Montavon
  • Klaus-Robert Müller
  • Wojciech Samek
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 376)

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.

Keywords

Deep neural networks Non-linear explanations 

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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Alexander Binder
    • 1
  • Sebastian Bach
    • 2
  • Gregoire Montavon
    • 3
  • Klaus-Robert Müller
    • 3
  • Wojciech Samek
    • 2
  1. 1.ISTD PillarSingapore University of Technology and DesignSingaporeSingapore
  2. 2.Machine Learning GroupFraunhofer HHIBerlinGermany
  3. 3.Machine Learning GroupTU BerlinBerlinGermany

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