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arXiv · 2003.11830

A lower bound for the ELBO of the Bernoulli Variational Autoencoder

Robert Sicks·Ralf Korn·Stefanie Schwaar
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Abstract

We consider a variational autoencoder (VAE) for binary data. Our main innovations are an interpretable lower bound for its training objective, a modified initialization and architecture of such a VAE that leads to faster training, and a decision support for finding the appropriate dimension of the latent space via using a PCA. Numerical examples illustrate our theoretical result and the performance of the new architecture.

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Robert Sicks, Ralf Korn, Stefanie Schwaar. 2020-03-26. A lower bound for the ELBO of the Bernoulli Variational Autoencoder. https://arxiv.org/abs/2003.11830

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