  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.
