  Constructing powerful generative models for natural images is a challenging
task. PixelCNN models capture details and local information in images very well
but have limited receptive field. Variational autoencoders with a factorial
decoder can capture global information easily, but they often fail to
reconstruct details faithfully. PixelVAE combines the best features of the two
models and constructs a generative model that is able to learn local and global
structures. Here we introduce PixelVAE++, a VAE with three types of latent
variables and a PixelCNN++ for the decoder. We introduce a novel architecture
that reuses a part of the decoder as an encoder. We achieve the state of the
art performance on binary data sets such as MNIST and Omniglot and achieve the
state of the art performance on CIFAR-10 among latent variable models while
keeping the latent variables informative.
