Methods for interpreting and understanding deep neural networks
- Citation data:
Digital Signal Processing, ISSN: 1051-2004, Vol: 73, Page: 1-15
- Publication Year:
- Computer Science; Engineering; Computer Science - Machine Learning; Statistics - Machine Learning
- Most Recent Tweet View All Tweets
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.