Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: This paper proposes UniGAN a new approach to alleviate mode collapse in GANs. Assuming the manifold hypothesis, the authors motivate training a generator with uniform distribution over the data manifold M. They encourage the uniform distribution by arguing that samples on the manifold M are equally accepted as real samples for training GANs. UniGAN restricts the generator to be Normalizing Flow (NF) based to perform effective and simple regularization in pursuit of a uniform generator. Authors also propose a new measure of performance of GANs and show the effectiveness of their methods on several benchmark datasets experimentally.
strengths_and_weaknesses: Pros:

1- the idea of the paper clearly has been explained.

2- The idea is novel.

3-The paper has solid theoretical results.

4- A large set of experiments has been done.

Cons:

One of the main reasons GANs have been preferred to other generative models despite mode collapse is their sample quality. However, the current paper restricts the generator to an NF-based model, which restricts the flexibility of the generator and consequently lowers the quality of samples. This also can be shown by looking at the Inception score of the trained model on CIFAR; UniGAN achieve IS of less than 3.9; however, PacGAN archives IS of more than 6 (please refer to Self-Cond-GAN or PGMGAN) when used with proper architecture.

Also, I have not convinced necessarily a uniform distribution is necessarily better. For an instant, consider toy data of 1D mixture of normals distribution. The support of this distribution is the entire R, and it is not ideal to have a uniform distribution over the entire space. Instead, one may prefer some samples to others.
questions: Please look at my above comments.
limitations: Yes
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: The paper proposes a simple yet effective way to mitigate the mode collapse issue of GANs. To this end the authors introduce the generator uniformity property, which is utilized to regularize a flow-based generator. Lastly, a new form of diversity is introduced, labelled uniform diversity.
strengths_and_weaknesses: Strength:

Although I am not particularly experienced in this particular sub-area (i.e., mitigating mode-collapse in GANs), I appreciate the effort the authors put into the technical part of the motivation. The main idea is clear and well motivated. The authors also made sure to thoroughly evaluate their model against a number baselines and metrics, which highlight the efficacy of the method.

Weakness: 

Although I don’t think the paper has any strong weakness, I would like to see how the approach performs on other mode-coverage benchmarks, e.g.,
- Fashion-MNIST and partial MNIST of [1]
- Stacked MNIST of  [2]

Lastly, I would also appreciate a discussion on the ethical considerations of this work, especially since it touches the task of generative modelling of faces.

[1]. Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach

[2] . VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
questions: I suggest the authors follow my suggestions regarding further evaluation as well as the inclusion of a discussion on ethical considerations.
limitations: The authors have provided adequate analysis of the limitations of the work. However, I would like to see a discussion on the societal impact of the work.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: In this paper, the authors propose the maximization of the uniform diversity of the generative distribution as a means for avoiding mode collapse. The main idea relies on the diffeomorphism between input uniform distribution and the samples that can be obtained using a normalizing flow -based generator.  The key insight is due to this property being preserved, one can maximize the uniform diversity. 

The paper provides useful theoretical and empirical analysis results that validate their work
strengths_and_weaknesses: Strengths:
1) The use of uniform diversity as a measure to evaluate mode collapse in GANs is interesting. The ideas are also related to the Epsilon-Delta analysis of PacGAN
2) Thorough evalution of their work by considering various existing techniques to avoid mode-collapse and showing that incorporating uniform diversity improves their performance
3) Detailed theoretical analysis of their work with detailed results being provided in the supplementary material.

Weakness:
1) The main observation is that the existing uniform diversity can be achieved if one obtains the diffeomorphism between the distribution and the sample space. However, this exists only in cases such as normalizing flows. In general, normalizing flows have invertible properties but suffer from being inferior in terms of sample generation. It is not clear, how exactly this limitation is addressed in this work.
2) As observed, the normalizing flows are limited in terms of the size of the latent space being exactly the same as the original sample space. The method adopted to solve this by zero-padding appears to be a heuristic. This needs to be better understood.
questions: It will be useful if the authors could comment on the weaknesses mentioned above. 
limitations: The negative social impact are not particularly applicable.

The limitations to some extent are addressed in the supplementary material.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper proposes a new type of generative diversity named uniform diversity, and thus introduce the corresponding $\mu$-mode collapse. And the authors propose a new framework called UniGAN to learn the uniform generator. This framework is formulated with a normalizing flow based generator and a uniformity regularization. Moreover, the authors also propose a new metric called udiv to estimate the uniform diversity. 
strengths_and_weaknesses: Strengths:
This paper proposes a new type of uniform diversity, and analyze the corresponding importance and behavior. And the proposed UniGAN shows great performance under the metric of udiv.

Weakness:
1. The experiments are conducted with small image resolution. And the uniform diversity on the high resolution images is not analyzed. The authors can combine the framework with some up-sampling layers to analyze the uniform diversity with the increase of image resolution.

2. The analysis results of Fig.5 can be made on other datasets to demonstrate the correctness of the conclusion on different datasets, like FFHQ, AFHQ, etc.

3. Will the proposed uniformity regularization suitable for dealing with other types of mode collapses? The relations between other types of mode collapses can be analyzed.


questions: Please refer to the weakness.
limitations: More experimental results, especially the visual analysis can be provided.
review_title: null

Meta-review: metareview: This paper proposes UniGAN to alleviate mode collapse in GANs. They encourage the uniform distribution by arguing that samples on the manifold are equally accepted as real samples for training GANs. 

The paper is comprehensive in both theory and experimental results. It receives average rating score 6, leading to an ``Accept'' decision. 

To further improve the impact of this paper, I suggest the authors to study it in the context of modern SoTA image generation models in the future. Hopefully, It may help the GAN-based model family [1,2,3] to improve the performance, in the competition with diffusion-model, auto-regressive models.

References:
- [1] Alias-Free Generative Adversarial Networks (StyleGAN3)
- [2] LAFITE: Towards Language-Free Training for Text-to-Image Generation
- [3] ViTGAN: Training GANs with Vision Transformers
confidence: Less certain
Decision: Accept