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Entropy 2017, 19(9), 474;

The Partial Information Decomposition of Generative Neural Network Models

Corti, Nørrebrogade 45E 2, 2200 Copenhagen N, Denmark
Department of Computing, Imperial College London, London SW7 2RH, UK
These authors contributed equally to this work.
Authors to whom correspondence should be addressed.
Received: 8 July 2017 / Revised: 13 August 2017 / Accepted: 1 September 2017 / Published: 6 September 2017
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In this work we study the distributed representations learnt by generative neural network models. In particular, we investigate the properties of redundant and synergistic information that groups of hidden neurons contain about the target variable. To this end, we use an emerging branch of information theory called partial information decomposition (PID) and track the informational properties of the neurons through training. We find two differentiated phases during the training process: a first short phase in which the neurons learn redundant information about the target, and a second phase in which neurons start specialising and each of them learns unique information about the target. We also find that in smaller networks individual neurons learn more specific information about certain features of the input, suggesting that learning pressure can encourage disentangled representations. View Full-Text
Keywords: partial information decomposition; neural networks; information theory partial information decomposition; neural networks; information theory

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Tax, T.M.; Mediano, P.A.; Shanahan, M. The Partial Information Decomposition of Generative Neural Network Models. Entropy 2017, 19, 474.

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