Most deep latent factor models choose simple priors for simplicity, tractability
 or not knowing what prior to use. Recent studies show that the choice of
 the prior may have a profound effect on the expressiveness of the model,
 especially when its generative network has limited capacity. In this paper, we propose to learn a proper prior from data for adversarial autoencoders
 (AAEs). We introduce the notion of code generators to transform manually selected
 simple priors into ones that can better characterize the data distribution. Experimental results show that the proposed model can generate better image quality and learn better disentangled representations than
 AAEs in both supervised and unsupervised settings. Lastly, we present its
 ability to do cross-domain translation in a  text-to-image synthesis task.
Learning Priors for Adversarial Autoencoders Proposes a simple extension of adversarial auto-encoders for conditional image generation. Focuses on adversarial autoencoders and introduces a code generator network to transform a simple prior into one that together with the generator can better fit the data distribution