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One of the major shortcomings of variational autoencoders is the inability toproduce generations from the individual modalities of data originating frommixture distributions. This is primarily due to the use of a simple isotropicGaussian as the prior for the latent code in the ancestral sampling procedurefor the data generations. We propose a novel formulation of variationalautoencoders, conditional prior VAE (CP-VAE), which learns to differentiatebetween the individual mixture components and therefore allows for generationsfrom the distributional data clusters. We assume a two-level generative processwith a continuous (Gaussian) latent variable sampled conditionally on adiscrete (categorical) latent component. The new variational objectivenaturally couples the learning of the posterior and prior conditionals, and thelearning of the latent categories encoding the multimodality of the originaldata in an unsupervised manner. The data-dependent conditional priors are thenused to sample the continuous latent code when generating new samples from theindividual mixture components corresponding to the multimodal structure of theoriginal data. Our experimental results illustrate the generative performanceof our new model comparing to multiple baselines.

Improving VAE generations of multimodal data through data-dependent conditional priors