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arXiv · 2004.02581

Variational auto-encoders with Student's t-prior

Najmeh Abiri·Mattias Ohlsson
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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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Najmeh Abiri, Mattias Ohlsson. 2020-04-06. Variational auto-encoders with Student's t-prior. https://arxiv.org/abs/2004.02581

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