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

## TL;DR

This paper introduces an embedding-reparameterization technique to incorporate manifold-valued latent variables into VAEs, aiming to enhance their flexibility and capacity beyond Gaussian priors.

## Contribution

The paper proposes a novel embedding-reparameterization method for integrating arbitrary manifold-valued variables into VAE models, expanding their modeling capabilities.

## Key findings

- Demonstrated the technique on a toy benchmark problem
- Showed potential for improved modeling capacity
- Work is ongoing, with further validation needed

## 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.