Information Theoretic-Learning Auto-Encoder

Eder Santana; Matthew Emigh; Jose C Principe

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arXiv · 1603.06653

Information Theoretic-Learning Auto-Encoder

Eder Santana·Matthew Emigh·Jose C Principe

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Abstract

We propose Information Theoretic-Learning (ITL) divergence measures for variational regularization of neural networks. We also explore ITL-regularized autoencoders as an alternative to variational autoencoding bayes, adversarial autoencoders and generative adversarial networks for randomly generating sample data without explicitly defining a partition function. This paper also formalizes, generative moment matching networks under the ITL framework.

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Eder Santana, Matthew Emigh, Jose C Principe. 2016-03-22. Information Theoretic-Learning Auto-Encoder. https://arxiv.org/abs/1603.06653

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