2020/06/04 by Miguel Vasco, Vasco, Miguel, Francisco S. Melo +3 · 4 citations
Computer Science · Mathematics · Psychology · #Artificial intelligence #Cognitive science #Computer science #Deep learning #FOS: Computer and information sciences #Generative grammar #Generative model #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Natural Language Processing Techniques #Political science #Psychology #Representation (politics) #Speech and dialogue systems #Topic Modeling #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2006.02991
published in arXiv (Cornell University) (Cornell University)
arxiv created 2020/06/04 · openalex publication_date 2020/06/04 · arxiv updated 2020/06/05 · openalex created_date 2020/06/12 · openalex updated_date 2026/07/28
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.