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 |
Keywords
- Activation maximization
- Deep neural networks
- Layer-wise relevance propagation
- Sensitivity analysis
- Taylor decomposition
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Statistics, Probability and Uncertainty
- Computational Theory and Mathematics
- Electrical and Electronic Engineering
- Artificial Intelligence
- Applied Mathematics
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Cite this
Montavon, G., Samek, W., & Müller, K. R. (2018). Methods for interpreting and understanding deep neural networks. Digital Signal Processing: A Review Journal, 73, 1-15. https://doi.org/10.1016/j.dsp.2017.10.011