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arXiv · 1905.04982

Learning Hierarchical Priors in VAEs

Alexej Klushyn·Nutan Chen·Richard Kurle·Botond Cseke·Patrick van der Smagt
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Abstract

We propose to learn a hierarchical prior in the context of variational autoencoders to avoid the over-regularisation resulting from a standard normal prior distribution. To incentivise an informative latent representation of the data, we formulate the learning problem as a constrained optimisation problem by extending the Taming VAEs framework to two-level hierarchical models. We introduce a graph-based interpolation method, which shows that the topology of the learned latent representation corresponds to the topology of the data manifold---and present several examples, where desired properties of latent representation such as smoothness and simple explanatory factors are learned by the prior.

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Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, Patrick van der Smagt. 2019-05-13. Learning Hierarchical Priors in VAEs. https://arxiv.org/abs/1905.04982

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