  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.
