Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: The authors study the sample diversity in GAN vs. diffusion models by conducting experiments using both synthetic and image data to explore the connection between mode collapse and sample diversity in GAN and diffusion models. 
strengths_and_weaknesses: Strength:
1. Paper is clearly written. Motivation is also quite clear.

Weakness:
1. Experiments were carried out on simplistic synthetic data or simplistic image data such as MNIST. In order to draw meaningful conclusions, especially with respect to sample diversity, more complex datasets need to be explored, such as ImageNet. 

suggested_changes: Add Imagenet experiments.
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: This paper is an empirical study focusing on the sample diversity of two representative generative models (i.e., GAN and DDPM), and the connection between it and mode collapse. Two sets of experiments are conducted on tiny datasets to demonstrate that DDPM is more powerful to generate diverse samples than GAN.
strengths_and_weaknesses: **Strengths:**

* This paper provides comprehensive preliminaries about generative models in the appendix.
* The paper is generally clear and well-structured.
* Experimental findings justify the conclusion.

**Weakness:**

* Most of the images are blurry when zooming in. The authors should use pdf instead of (high-resolution) png/jpg.
* The authors should clearly state which color is the result of GAN/DDPM in Figures 1, 2, and 4.
* The current experiments are only focusing on observing the results/phenomenon. Further (deeper) analyses are needed. For example, why DDPM is better than GAN in terms of sample diversity? *Either empirical or theoretical insights could significantly improve the paper.*

**Minor:**

* In Appendix C.1,

  * "They have proven to be very effective ..." -> "They have been proven".

  * The author is suggested to improve the writing, e.g., avoid writing long sentences. 

    > "The generator outputs a fake sample in real data space generated by taking in random noise in its latent space." in the caption of Figure 9. -> "By taking as input the random noise in the latent space, the generator produces a fake sample in the space of real data."

* In Appendix C.2,

  * "Reverse diffusion process... **form** ..." -> from.
  * "which will be **sued** to denoise the sample" -> used.
  * "..., **which** x0 is the input" -> where.

**Overall:**

* *Clarity:* Fair. The paper is clearly written. However, I suggest adding more discussions of relevant literature.

* *Correctness:* Good. Most of the paper is correct. But I didn't check the correctness of Appendix C.3, in which I am not an expert.

* *Reproducibility:* Good. The author doesn't provide the source code. But I think the results are reproducible based on the presented setups.

* *Follows basic requirements:* Yes, the authors follow the ICLR code of conduct.
suggested_changes: Please see the weakness and minor sections. About the writing, I suggest the authors trying with AI-assisted rewriting tools, such as Grammarly and ChatGPT.
review_title: null

Decision: Invite to present