Paper:
Any autoencoder network can be turned into a generative model by imposing an arbitrary prior distribution on its hidden code vector. Variational Autoencoder uses a KL divergence penalty to impose the prior, whereas Adversarial Autoencoder uses generative adversarial networks. A straightforward modification of Adversarial Autoencoder is to replace the adversary by maximum mean discrepancy (MMD) test. This replacement leads to a new type of probabilistic autoencoder, which is also discussed in our paper.
However, an essential challenge remains in both of these probabilistic autoencoders, namely that the only source of randomness at the output of encoder, is the training data itself. Lack of enough stochasticity can make the optimization problem nontrivial. As a result, they can lead to degenerate solutions where the generator collapses into sampling only a few modes.
Our proposal is to replace the adversary of adversarial autoencoder by a space of stochastic functions. This replacement introduces a a new source of randomness, which can be considered as a continuous control for encouraging explorations. This prevents the adversary from fitting too closely to the generator and therefore leads to more diverse set of generated samples. Consequently, the decoder serves as a better generative network, which unlike MMD nets scales linearly with the amount of data. We provide mathematical and empirical evidence on how this replacement outperforms the pre-existing architectures.

Review:
Thank you for the feedback, and I have read it.
The authors claimed that they used techniques in [6] in which I am not an expert for this. However I cannot find the comparison that the authors mentioned in the feedback, so I am not sure if the claim is true.
I still recommend rejection for the paper, and as I said in the first review, the paper is not mature enough.
==== original review ===
The paper describes a generative model that replaces the GAN loss in the adversarial auto-encoder with MMD loss. Although the author claim the novelty as adding noise to the discriminator, it seems to me that at least for the RBF case it just does the following:
1. write down MMD as an integral probability metric (IPM)
2. say the test function, which originally should be in an RKHS, will be approximated using random feature approximations.
Although the authors explained the intuition a bit and showed some empirical results, I still don't see why this method should work better than directly minimising MMD. Also it is not preferred to look at the generated images and claim diversity, instead it's better to have some kind of quantitative metric such as the inception score.
Finally, given the fact that we have too many GAN related papers now, I don't think the innovation contained in the paper (which is using random features) is good enough to be published at ICLR. Also the paper is not clearly written, and I would suggest better not to copy-past paragraphs in the abstract and intro.
That said, I would welcome for the authors feedback and see if I have misunderstood something.