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

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).

Appendix for the IEEE FUSION 2019 submission on multi-modal variational Autoencoders for sensor fusion