This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. It introduces some recently proposed techniques of interpretation, along with theory, tricks and recommendations, to make most efficient use of these techniques on real data. It also discusses a number of practical applications.
@article{arxiv.1706.07979,
title = {Methods for Interpreting and Understanding Deep Neural Networks},
author = {Grégoire Montavon and Wojciech Samek and Klaus-Robert Müller},
journal= {arXiv preprint arXiv:1706.07979},
year = {2017}
}