  We study the problem of multimodal generative modelling of images based on
generative adversarial networks (GANs). Despite the success of existing
methods, they often ignore the underlying structure of vision data or its
multimodal generation characteristics. To address this problem, we introduce
the Dirichlet prior for multimodal image generation, which leads to a new
Latent Dirichlet Allocation based GAN (LDAGAN). In detail, for the generative
process modelling, LDAGAN defines a generative mode for each sample,
determining which generative sub-process it belongs to. For the adversarial
training, LDAGAN derives a variational expectation-maximization (VEM) algorithm
to estimate model parameters. Experimental results on real-world datasets have
demonstrated the outstanding performance of LDAGAN over other existing GANs.
