Kati8/ICLR_2023_2025 huggingface dataset:
authors: Zijing Ou
authors: Mingtian Zhang
authors: Andi Zhang
authors: Tim Z. Xiao
authors: Yingzhen Li
authors: David Barber
reviews: 
Comments: The authors propose an $x_t$-dependent diagonal covariance estimation method that directly learns from score function. This leads to improved efficiency and more stable/robust denoising, which in turn improves the image generation quality.
Strengths: 1. Very clear and comprehensive presentation, easy to read and follow.
2. The proposed method is a general method that applies to various settings.
3. The description of experimental details is generally comprehensive.
Weaknesses: 1. The idea seems both incremental and way-too simple, which is not a fundamental change in the covariance estimation that would lead to a revolutionized/big change in the performance and/or efficiency. It will have a limited impact on the community.

2. The experimental results seem not extraordinary. It does not fundamentally improve the performance (generation quality), which is understandable since covariance is more about sampling efficiency and sample diversity. However, even in terms of the sampling diversity and efficiency, such as indicated by Table 4, the proposed method does not significantly beat strong baselines. Moreover, there are no error bars or uncertainty in the experimental results.
Recommendation: 8
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 4
reviews: 
Comments: The authors introduce an optimal covariance matching objective based on score functions from pertained diffusion models to learn the covariance of the Gaussian transition kernel. 
The proposed method is superior at covariance estimation in terms of  less storage consumption and less number of network evaluations. 
Empirical results indicate that the proposed objective enhances performance in both sampling quality (NLL, FID) and efficiency (number of sample steps).
Strengths: - The paper is well-organized, results are clearly presented, and the technique derivations are solid. 
- Overall paper organization is very smooth, the methodology part is easy to follow, and experiments are well-designed and coupled with the proposed methodology. e.g. from figure3, table 3 and table 5, I can see that the improved sample quality can be directly attributed from better covariance estimation. 
- Unlike the previous works which are of high requirements on storage and network evaluation in covariance estimate (especially the Hessian term), the proposed OCM barely needs 1 network evaluation for each sampling step, so that the method improves number of sampling steps without sacrificing too much sampling speed on covariance estimate.
Weaknesses: - The authors show that the proposed OCM improves covariance estimation for DDPM and DDIM, which results in improved sample quality and better efficiency. There are many other stochastic and deterministic samplers with different sampling schedules, does the improved covariance helps with all these samplers?
Recommendation: 8
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 4