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Information Theoretic-Learning Auto-Encoder - 2016 PDF

8 Pages·2020·English
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by ['ajgallego']| 2020| 8 pages| English

About Information Theoretic-Learning Auto-Encoder - 2016

This document proposes an Information Theoretic-Learning Auto-Encoder (ITL-AE) as an alternative to variational autoencoders and generative adversarial networks for generating sample data from a distribution without explicitly defining a partition function. The ITL-AE uses Information Theoretic-Learning divergence measures as a way to regularize the encoder network to match a desired prior distribution, which allows sampling from the prior to generate new data. The document reviews relevant Information Theoretic-Learning concepts like Parzen density estimation and Renyi's entropy measures that are used to calculate the ITL divergences.

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Author:['ajgallego']
Publication Year:2020
Pages:8
Language:English
Format:PDF
Price:FREE
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