  Multimodal learning for generative models often refers to the learning of
abstract concepts from the commonality of information in multiple modalities,
such as vision and language. While it has proven effective for learning
generalisable representations, the training of such models often requires a
large amount of "related" multimodal data that shares commonality, which can be
expensive to come by. To mitigate this, we develop a novel contrastive
framework for generative model learning, allowing us to train the model not
just by the commonality between modalities, but by the distinction between
"related" and "unrelated" multimodal data. We show in experiments that our
method enables data-efficient multimodal learning on challenging datasets for
various multimodal VAE models. We also show that under our proposed framework,
the generative model can accurately identify related samples from unrelated
ones, making it possible to make use of the plentiful unlabeled, unpaired
multimodal data.
