text: Recently some interesting work on a role of prior in deep generative models has been presented. The choice of prior may have an impact on the expressiveness of the model [Hoffman and Johnson, 2016]. A few existing work presents methods for learning priors from data for variational autoencoders [Goyal et al., 2017] [Tomczak and Welling, 2017]. The work, "VAE with a VampPrior," [Tomczak and Welling, 2017] is missing in references. The current work focuses on adversarial autoencoder (AAE) and introduces a code generator network to transform a simple prior into one that together with the generator can better fit the data distribution. Adversarial loss is used to train the code generator network, allowing the output of the network could be any distribution. I think the method is quite simple but interesting approach to improve AAEs without hurting the reconstruction. The paper is well written and is easy to read. The method is well described. However, what is missing in this paper is an analysis of learned priors, which help us to better understand its behavior. The model is evaluated qualitatively only. What about quantitative evaluation?