  Learning from multimodal data is an important research topic in machine
learning, which has the potential to obtain better representations. In this
work, we propose a novel approach to generative modeling of multimodal data
based on generative adversarial networks. To learn a coherent multimodal
generative model, we show that it is necessary to align different encoder
distributions with the joint decoder distribution simultaneously. To this end,
we construct a specific form of the discriminator to enable our model to
utilize data efficiently, which can be trained constrastively. By taking
advantage of contrastive learning through factorizing the discriminator, we
train our model on unimodal data. We have conducted experiments on the
benchmark datasets, whose promising results show that our proposed approach
outperforms the-state-of-the-art methods on a variety of metrics. The source
code will be made publicly available.
