  Multimodal generative models should be able to learn a meaningful latent
representation that enables a coherent joint generation of all modalities
(e.g., images and text). Many applications also require the ability to
accurately sample modalities conditioned on observations of a subset of the
modalities. Often not all modalities may be observed for all training data
points, so semi-supervised learning should be possible. In this study, we
propose a novel product-of-experts (PoE) based variational autoencoder that
have these desired properties. We benchmark it against a mixture-of-experts
(MoE) approach and an approach of combining the modalities with an additional
encoder network. An empirical evaluation shows that the PoE based models can
outperform the contrasted models. Our experiments support the intuition that
PoE models are more suited for a conjunctive combination of modalities.
