Embedding-reparameterization procedure for manifold-valued latent variables in generative models
Machine Learning
2018-12-10 v1 Machine Learning
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.
Keywords
Cite
@article{arxiv.1812.02769,
title = {Embedding-reparameterization procedure for manifold-valued latent variables in generative models},
author = {Eugene Golikov and Maksim Kretov},
journal= {arXiv preprint arXiv:1812.02769},
year = {2018}
}
Comments
Presented at Bayesian Deep Learning workshop (NeurIPS 2018)