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).
- Publication:
-
arXiv e-prints
- Pub Date:
- March 2019
- DOI:
- 10.48550/arXiv.1903.07303
- arXiv:
- arXiv:1903.07303
- Bibcode:
- 2019arXiv190307303K
- Keywords:
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- Computer Science - Machine Learning;
- Statistics - Machine Learning
- E-Print:
- Appendix for the IEEE FUSION 2019 submission on multi-modal variational Autoencoders for sensor fusion