Following the author response and discussion, all reviewers had an overall positive impression of the paper, highlighting some salient features: studies an interesting and under-explored problem setting, namely, PU learning where the positive samples are from a distribution unrelated to that of the target distribution the proposed method is equipped with theoretical guarantees, and is demonstrated to perform well empirically Some areas for improvement include: - the lack of comparison against bPU. The argument of such techniques making an assumption that does not hold is fine, but how well do they perform on the tasks considered here? The authors are encouraged to incorporate these in a revised version.

This paper combines the two and conducts adversarial training by applying the regularization term on unlabeled data. The theoretical and empirical analysis are new. However, some crucial points are not well addressed. It is observed that the proposed method (UAT) behaves differently on CIFAR10 and SVHN (Fig.1), wrt m. On CIFAR10, it outperforms others starting from m 4k.

Clarity: Overall the paper is very clear. The authors did an excellent job. Equation 5 - I am confused on a few things. The notation P(y x; theta) is confusing because the semicolon implies that theta is a vector and not a random vector, however, the conditional distribution of theta is given P(theta G). So what is the point of the semicolon? Also, there is a typo in Equation 5 I think because the entropy term is not defined correctly.

R#2 and R#3 generally liked the paper. R#1 has a brief review that raised concern on novelty of the method. The rebuttal well addressed the concerns and made all reviewers increase their score. We have collected comments from an additional reviewer, who pointed out more issues on writing and the theoretical results (see blew). We advise the authors to take efforts to address these issues in the revision.

Originality: -Although the Hardt paper has suggested the use of this approach, the paper claims that it's the first to actually show this is indeed possible. Quality: -The assumptions made about the model are very well justified. The discussion after each assumption provided the context as to why the assumption makes sense and why the assumption is needed to study their model. These discussion as a result provided very good intuition and set up the stage for the proof. Clarity: -Overall, the paper have a very smooth flow, whether it be discussion of their assumptions or their remarks.

Three reviewers who are all good experts for this paper found the paper interesting, novel, compelling, and well-written. With such a difficult topic as fairness, it was particularly helpful that the authors were able to discuss their assumptions, results, and proofs so clearly, and that definitely adds value to the work. The authors' response was appreciated and was found to be helpful, but reviewers expressed some concern in discussion about adding too many new results they didn't have a chance to review, so while we hope the authors can address some of the reviewers suggestions in the final paper, they are encouraged not to add too much stuff that wasn't reviewed, but instead to consider expanding on some of that for a follow-on submission.

I have read it carefully. The new experiments look good, but the authors do not seem to respond to my concern over SSIM metric between unpaired images. I keep my original review and rating. Given all the prior works that smooth GAN training, the idea that integrates image quality assessment metrics with GANs sounds interesting. From the experiment samples, it seems that the quality aware gan does improve the sample quality, the generated CelebA and STL images look sharp.

The paper proposes a novel way to regularize training of deep adversarial generative models for natural images. The proposal is based on using the image quality metrics. While many different ways of stabilizing and regularizing GAN training were proposed in prior work, most of which based on various gradient penalties related to the Lipschitzness, this submission proposes an idea which is significantly different and novel. The paper evaluates the new method on three reasonably challenging datasets (CIFAR-10, STL-10, CelebA) and quantitatively shows objective advantages to other methods (in terms of FID and IS). The field of GANs and in particular various ways to stabilize their training has been recently attracting perhaps excessive amount of attention with many papers proposing multiple methods very similar in nature.

This paper received reviews from 3 expert reviewers. The reviewers appreciated the interesting task (few cost saliency detection) and the use of self-paced learning combined with generative adversarial learning. After considering the authors' response, the reviewers refined their positions on the paper. R2's comments regarding semi-supervised learning remain valid. The authors would be encouraged to refine the presentation of this and use of terms.

Summary and Contributions: This work proposes to address the problem of limited data in GAN training with discriminator augmentation (DA), a technique which enables most standard data augmentation techniques to be applied to GANs without leaking them into the learned distribution. The method is simple, yet effective: non-leaking differentiable transformations are applied to real and fake images before being passed through the discriminator, both during discriminator and generator updates. To make transformations non-leaking, it is proposed to apply them with some probability p 1 such that the discriminator will eventually be able to discern the true underlying distribution. One challenge introduced with this technique is that different datasets require different amounts of augmentation depending on their size, and as such, expensive grid search is required for optimization. To eliminate the need for this search step an adaptive version called adaptive discriminator augmentation (ADA) is introduced.