  Multi-modal generative models represent an important family of deep models,
whose goal is to facilitate representation learning on data with multiple views
or modalities. However, current deep multi-modal models focus on the inference
of shared representations, while neglecting the important private aspects of
data within individual modalities. In this paper, we introduce a disentangled
multi-modal variational autoencoder (DMVAE) that utilizes disentangled VAE
strategy to separate the private and shared latent spaces of multiple
modalities. We specifically consider the instance where the latent factor may
be of both continuous and discrete nature, leading to the family of general
hybrid DMVAE models. We demonstrate the utility of DMVAE on a semi-supervised
learning task, where one of the modalities contains partial data labels, both
relevant and irrelevant to the other modality. Our experiments on several
benchmarks indicate the importance of the private-shared disentanglement as
well as the hybrid latent representation.
