Learning Hierarchical Priors in VAEs

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

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. Furthermore, we validate our approach on standard datasets, obtaining state-of-the-art test log-likelihoods.

CMT Num: 1661