Embedding-reparameterization procedure for manifold-valued latent variables in generative models

Abstract

Conventional prior for Variational Auto-Encoder (VAE) is a Gaussian distribution. Recent works demonstrated that choice of prior distribution affects learning capacity of VAE models. We propose a general technique (embedding-reparameterization procedure, or ER) for introducing arbitrary manifold-valued variables in VAE model. We compare our technique with a conventional VAE on a toy benchmark problem. This is work in progress.

Presented at Bayesian Deep Learning workshop (NeurIPS 2018)