Poster
Learning Hierarchical Priors in VAEs
Alexej Klushyn · Nutan Chen · Richard Kurle · Botond Cseke · Patrick van der Smagt
East Exhibition Hall B + C #153
Keywords: [ Latent Variable Models; Probabili ] [ Probabilistic Methods; Probabilistic Methods -> Hierarchical Models; Probabilistic Methods ] [ Deep Learning ] [ Generative Models ]
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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