Real or Not Real, that is the Question
While generative adversarial networks (GAN) have been widely adopted in various topics, in this paper we generalize the standard GAN to a new perspective by treating realness as a random variable that can be estimated from multiple angles. In this generalized framework, referred to as RealnessGAN, the discriminator outputs a distribution as the measure of realness. While RealnessGAN shares similar theoretical guarantees with the standard GAN, it provides more insights on adversarial learning. More importantly, compared to multiple baselines, RealnessGAN provides stronger guidance for the generator, achieving improvements on both synthetic and real-world datasets. Moreover, it enables the basic DCGAN architecture to generate realistic images at 1024*1024 resolution when trained from scratch.
The paper proposes to improve upon GANs by considering to infer the distribution of realness instead of binary true/false labels in the discriminator side. They carry out this idea with some theoretical arguments, and their method is shown to perform very well in empirical experiments on one synthetic dataset, and three real world datasets:CelebA, CIFAR-10, and FFHQ. Overall I feel this is a well presented paper with a simple yet interesting idea and solid results. The authors are encouraged to share their code and results to public.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Real or Not Real, that is the Question
While generative adversarial networks (GAN) have been widely adopted in various topics, in this paper we generalize the standard GAN to a new perspective by treating realness as a random variable that can be estimated from multiple angles. In this generalized framework, referred to as RealnessGAN, the discriminator outputs a distribution as the measure of realness. While RealnessGAN shares similar theoretical guarantees with the standard GAN, it provides more insights on adversarial learning. More importantly, compared to multiple baselines, RealnessGAN provides stronger guidance for the generator, achieving improvements on both synthetic and real-world datasets. Moreover, it enables the basic DCGAN architecture to generate realistic images at 1024*1024 resolution when trained from scratch.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper proposes to improve upon GANs by considering to infer the distribution of realness instead of binary true/false labels in the discriminator side. They carry out this idea with some theoretical arguments, and their method is shown to perform very well in empirical experiments on one synthetic dataset, and three real world datasets:CelebA, CIFAR-10, and FFHQ. Overall I feel this is a well presented paper with a simple yet interesting idea and solid results. The authors are encouraged to share their code and results to public.
assistant
The paper proposes to improve upon GANs by considering to infer the distribution of realness instead of binary true/false labels in the discriminator side. They carry out this idea with some theoretical arguments, and their method is shown to perform very well in empirical experiments on one synthetic dataset, and three real world datasets:CelebA, CIFAR-10, and FFHQ. Overall I feel this is a well presented paper with a simple yet interesting idea and solid results. The authors are encouraged to share their code and results to public.