Variational auto-encoders with Student's t-prior

• URL: http://arxiv.org/abs/2004.02581v1
• Date: Mon, 6 Apr 2020 11:54:20 GMT
• Title: Variational auto-encoders with Student's t-prior
• Authors: Najmeh Abiri and Mattias Ohlsson
• Abstract summary: We propose a new structure for the variational auto-encoders (VAEs) prior. All distribution parameters are trained, thereby allowing for a more robust approximation of the underlying data distribution.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: We propose a new structure for the variational auto-encoders (VAEs) prior, with the weakly informative multivariate Student's t-distribution. In the proposed model all distribution parameters are trained, thereby allowing for a more robust approximation of the underlying data distribution. We used Fashion-MNIST data in two experiments to compare the proposed VAEs with the standard Gaussian priors. Both experiments showed a better reconstruction of the images with VAEs using Student's t-prior distribution.

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