Abstract
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. As a tutorial paper, the set of methods covered here is not exhaustive, but sufficiently representative to discuss a number of questions in interpretability, technical challenges, and possible applications. The second part of the tutorial focuses on the recently proposed layer-wise relevance propagation (LRP) technique, for which we provide theory, recommendations, and tricks, to make most efficient use of it on real data.
| Original language | English |
|---|---|
| Pages (from-to) | 1-15 |
| Number of pages | 15 |
| Journal | Digital Signal Processing: A Review Journal |
| Volume | 73 |
| DOIs | |
| Publication status | Published - 2018 Feb 1 |
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Keywords
- Activation maximization
- Deep neural networks
- Layer-wise relevance propagation
- Sensitivity analysis
- Taylor decomposition
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
Cite this
Methods for interpreting and understanding deep neural networks. / Montavon, Grégoire; Samek, Wojciech; Muller, Klaus.
In: Digital Signal Processing: A Review Journal, Vol. 73, 01.02.2018, p. 1-15.Research output: Contribution to journal › Review article
}
TY - JOUR
T1 - Methods for interpreting and understanding deep neural networks
AU - Montavon, Grégoire
AU - Samek, Wojciech
AU - Muller, Klaus
PY - 2018/2/1
Y1 - 2018/2/1
N2 - 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. As a tutorial paper, the set of methods covered here is not exhaustive, but sufficiently representative to discuss a number of questions in interpretability, technical challenges, and possible applications. The second part of the tutorial focuses on the recently proposed layer-wise relevance propagation (LRP) technique, for which we provide theory, recommendations, and tricks, to make most efficient use of it on real data.
AB - 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. As a tutorial paper, the set of methods covered here is not exhaustive, but sufficiently representative to discuss a number of questions in interpretability, technical challenges, and possible applications. The second part of the tutorial focuses on the recently proposed layer-wise relevance propagation (LRP) technique, for which we provide theory, recommendations, and tricks, to make most efficient use of it on real data.
KW - Activation maximization
KW - Deep neural networks
KW - Layer-wise relevance propagation
KW - Sensitivity analysis
KW - Taylor decomposition
UR - http://www.scopus.com/inward/record.url?scp=85033371689&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85033371689&partnerID=8YFLogxK
U2 - 10.1016/j.dsp.2017.10.011
DO - 10.1016/j.dsp.2017.10.011
M3 - Review article
VL - 73
SP - 1
EP - 15
JO - Digital Signal Processing: A Review Journal
JF - Digital Signal Processing: A Review Journal
SN - 1051-2004
ER -