MHVAE: a Human-Inspired Deep Hierarchical Generative Model for Multimodal Representation Learning

2020/06/04 by Vasco, Miguel, Melo, Francisco S., Paiva, Ana
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · doi:10.48550/arxiv.2006.02991

Abstract

Humans are able to create rich representations of their external reality. Their internal representations allow for cross-modality inference, where available perceptions can induce the perceptual experience of missing input modalities. In this paper, we contribute the Multimodal Hierarchical Variational Auto-encoder (MHVAE), a hierarchical multimodal generative model for representation learning. Inspired by human cognitive models, the MHVAE is able to learn modality-specific distributions, of an arbitrary number of modalities, and a joint-modality distribution, responsible for cross-modality inference. We formally derive the model's evidence lower bound and propose a novel methodology to approximate the joint-modality posterior based on modality-specific representation dropout. We evaluate the MHVAE on standard multimodal datasets. Our model performs on par with other state-of-the-art generative models regarding joint-modality reconstruction from arbitrary input modalities and cross-modality inference.

Related

• Joint Multimodal Learning with Deep Generative Models
• Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models
• Multimodal Variational Autoencoder: a Barycentric View
• Disentangling by Partitioning: A Representation Learning Framework for\n Multimodal Sensory Data
• Generalizing Multimodal Variational Methods to Sets