NDLI: On constructing and analysing an interpretable brain model for the DNN based on hidden activity patterns

Content Provider	IEEE Xplore Digital Library
Author	Sim, Khe Chai
Copyright Year	2015
Description	Author affiliation: Computer Science Department, School of Computing, National University of Singapore, Singapore (Sim, Khe Chai)
Abstract	Deep Neural Network (DNN) has been well received as a powerful machine learning model in a wide range of pattern classification tasks. Despite its superior performance in handling complex real-world problems, DNNs have been used pretty much as a black box, without offering much insights in terms of how and why high quality classification performance has been achieved. To address this problem, this paper studies the DNN hidden unit activities and presents a novel interpretable DNN visualisation technique that projects the hidden units of the DNN onto a meaningful 2-dimensional subspace. The projected points are displayed with colours to reflect the activation values for the purpose of visualisation. In this paper, the proposed technique is used to visualise two DNN acoustic models trained on the multi-condition data from the Aurora 4 corpus. The technique is able to produce a two dimensional representation of the DNN "brain" with interpretable regions. It also accentuates the effect of how the behaviour of the hidden units changes across different layers.
Starting Page	22
Ending Page	29
File Size	3797131
Page Count	8
File Format	PDF
e-ISBN	9781479972913
DOI	10.1109/ASRU.2015.7404769
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Acoustics Entropy Brain modeling Shape Speech Visualization Training interpretability deep neural network visualisation
Content Type	Text
Resource Type	Article

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