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Image credit: Christian Lillelund on Kaggle
Deep neural networks have been successfully applied to problems in many domains. Understanding their inner workings with respect to feature selection and decision making, however, remains challenging and thus trained models are often regarded as black boxes. Layerwise Relevance Propagation (LRP) addresses this issue by finding those features that a model relies on, offering deeper understanding and interpretation of trained networks. This repository contains code and data used in Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals (https://arxiv.org/abs/1807.03418).
If you use the provided audioMNIST dataset for your project, please cite our paper:
@ARTICLE{becker2018interpreting,
author = {Becker, S\"oren and Ackermann, Marcel and Lapuschkin, Sebastian and M\"uller, Klaus-Robert and Samek, Wojciech},
title = {Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals},
journal = {CoRR},
volume = {abs/1807.03418},
year = {2018},
archivePrefix = {arXiv},
eprint = {1807.03418},
}
This open source contribution is part of DagsHub x Hacktoberfest
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