Learning Multimodal Latent Generative Models with Energy-Based Prior
This work addresses the problem of limited expressiveness in prior distributions for multimodal generative models, which is important for researchers working on cross-modal generation and representation learning.
The paper proposes a novel framework that integrates a multimodal latent generative model with an Energy-Based Model (EBM) to address the limitations of standard unimodal priors in capturing diverse multimodal information. This joint training approach results in a more expressive prior and demonstrates superior generation coherence.
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.