Deep generative models are making progress in modeling complex, high-dimensional data in an unsupervised manner. Two promising approaches are variational autoencoders (VAEs) and generative adversarial networks (GANs). VAEs impose a prior on the code space to regularize and allow for sampling, while GANs use a generator and discriminator in an adversarial training procedure. Recent work has focused on extensions of these models, including conditional generation and combining aspects of VAEs and GANs. However, generative modeling of natural images remains challenging, with models still underfitting the complexity of unconstrained data.