Learning Multimodal Latent Generative Models with Energy-Based Prior

TL;DR

This paper introduces a novel multimodal generative model that integrates energy-based models as priors, resulting in more expressive representations and improved cross-modal generation coherence.

Contribution

It proposes a new framework combining multimodal latent models with EBMs, trained jointly via a variational scheme, enhancing prior expressiveness.

Findings

Superior generation coherence demonstrated in experiments

More expressive and informative priors captured across modalities

Effective joint training of multimodal models with EBMs

Abstract

Multimodal generative models have recently gained significant attention for their ability to learn representations across various modalities, enhancing joint and cross-generation coherence. However, most existing works use standard Gaussian or Laplacian distributions as priors, which may struggle to capture the diverse information inherent in multiple data types due to their unimodal and less informative nature. Energy-based models (EBMs), known for their expressiveness and flexibility across various tasks, have yet to be thoroughly explored in the context of multimodal generative models. In this paper, we propose a novel framework that integrates the multimodal latent generative model with the EBM. Both models can be trained jointly through a variational scheme. This approach results in a more expressive and informative prior, better-capturing of information across multiple modalities. Our experiments validate the proposed model, demonstrating its superior generation coherence.