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

• URL: http://arxiv.org/abs/2006.02991v1
• Date: Thu, 4 Jun 2020 16:24:00 GMT
• Title: MHVAE: a Human-Inspired Deep Hierarchical Generative Model for Multimodal Representation Learning
• Authors: Miguel Vasco, Francisco S. Melo, Ana Paiva
• Abstract summary: We contribute the Multimodal Hierarchical Vari 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 and a joint-modality distribution, responsible for cross-modality inference. 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.
• Score: 8.70928211339504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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