# M$^2$VAE - Derivation of a Multi-Modal Variational Autoencoder Objective from the Marginal Joint Log-Likelihood

## TL;DR

This paper provides a detailed derivation of the training objective for Multi-Modal Variational Autoencoders (M$^2$VAE) based on the marginal joint log-likelihood, facilitating better understanding and implementation.

## Contribution

It offers a comprehensive derivation of the evidence lower bound for M$^2$VAE, clarifying the training process for multi-modal generative models.

## Key findings

- Derived a clear training objective for M$^2$VAE
- Enhanced understanding of multi-modal variational autoencoders
- Facilitated implementation of M$^2$VAE models

## Abstract

This work gives an in-depth derivation of the trainable evidence lower bound obtained from the marginal joint log-Likelihood with the goal of training a Multi-Modal Variational Autoencoder (M$^2$VAE).