Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: The paper proposes the novel DDIB for the image-to-image translation task, which diffuses&reversely diffuses in both source and target domains separately. This method only needs to independently train both diffusion processes and better safeguard dataset privacy. The paper also presents an ODE-based theoretic foundation to describe the latent space based translation as an entropy-regularized optimal transport. Experiments are conducted on several image translation tasks using synthetic and high-resolution image datasets.
strength_and_weaknesses: Strength: The ODE-based theoretic foundation is solid, which justifies the bridge idea based on the latent space. In terms of experiments, there are abundant results presented in the paper as well as in the supplementary materials. The synthetic image translation task nicely demonstrates the merit of this work.

Weaknesses:

While the author discusses the privacy protection of medical images, they lack corresponding experiments to confirm the translation performances on medical images.
While natural image patterns are usually varying and contain different foreground&background contexts even within one class, medical image translation focuses the detail preservation. Therefore, it is not clear if such a separately training method is capable to handle the task.

Considering the privacy protection problem, we can employ two well-pretrained auto-encoders, named A&B, for both source&target domains(denoted S and T) to obtain latent encodings A(S) and B(T), which mapping retains domain information mostly. Then we can utilize other image-to-image translation models (GAN-based or normalizing flow-based) to jointly train a model on latent encodings and protect data privacy. I am curious about the practical differences and performance differences between the DDIB and such a direct manner. Can the authors provide a detailed discussion?
clarity,_quality,_novelty_and_reproducibility: Clarity: The paper is nicely written.

Novelty: It is rather easy to conceive the idea of using the latent diffusion output as bridge. But putting it under a solid theorectic foundation is novel, offering the necessary justifcation.

Reproducibility: It is not impossible to reproduce the work. Supplying the codes will make it much easier.

Quality: A decent paper with theorectic contribution and abundant translation results.
summary_of_the_review: In sum, it is a decent paper with strengths in its theorectic foundings and extensive experiments. The synthetic image translation results look really impressive. But it lacks the results on say medical images, where privacy concern is real. I believe that the paper opens a new direction of applying the DDPM method to image translation across different domains.
review_title: null

final_score: rating: 10: strong accept, should be highlighted at the conference
review_content: summary_of_the_paper: This paper proposes an unpaired image-to-image translation method, which consists of two modified Denoising Diffusion Implicit
Models (DDIMs). The authors s theoretically demonstrate the modified DDIM as Schrodinger Bridges and name it DDIB. Experimental studies on both paired image-to-image translation and unpaired image-to-image translation are included. As far as my knowledge goes, the proposed work is the first diffusion model to successfully achieve the unpaired image-to-image translation with open resources (some unpublished works, such as UNIT-DDPM, do not have released codes). 
strength_and_weaknesses: Strength:
* The paper is well-written and well-organized.
* The idea of treating the modified DDIM as Schrodinger Bridges is novel.
*  The work is easy to follow. I have run the codes and found it could get stable results on small datasets, which can not be achieved by the SOTA general image-to-image translation model, Palette [A].  
To be honest, I have utilized it as a comparison baseline in my work now. 

Weakness:
* The comparisons included in experiments are not sufficient. Many baselines, such as MUNIT and Stargan should be included. Furthermore, it lacks quantitative comparisons. 

[A] Saharia, Chitwan, et al. "Palette: Image-to-image diffusion models." ACM SIGGRAPH 2022 Conference Proceedings. 2022.
clarity,_quality,_novelty_and_reproducibility: The paper is well-written with all concepts clearly introduced. And the proposed method is novel, cause I haven't seen any similar contributions in diffusion-based methods. Additionally, I am very confident to say this work is reproducible, cause I have run the codes.
summary_of_the_review: The authors theoretically demonstrate their proposed modified DDIM as Schrodinger Bridges. Experimental studies support their claimed contributions. As far as my knowledge goes, the proposed work is the first diffusion model to successfully achieve the unpaired image-to-image translation with open resources. Although this paper has some drawbacks in the comparison parts, it is novel and reproducible. I am very happy to see this impactful work published since it has been released for a while.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper proposes to do image translation by first converting source image with forward diffusion, and then run reverse diffusion in the target domain. Authors draw connection of this method with Schrodinger Bridge problem, demonstrated qualitative results on 2D synthetic datasets, color transfer and validated cycle consistency on image translations. For quantitative evaluations they compared pixe-wise MSE on paried datasets Facades and Maps and showed better results on one-direction translation but worse on the other direction. 
strength_and_weaknesses: Stength 
1. The paper is clearly written and easy to follow. 
2. The experiments showed inspiring image translation scenarios (image corloring, animal species translation and etc.)


Weakness
1. The proposed method is straightfoward application of diffusion model of limited novelty (e.g. using foward difussion (DDIM) to extract image latent for reconstuction has been explored before) 
2. The results on translation varies a lot depending on the source domain and target domain (e.g. the good examples of species translation are among the close related species while others do not preserve the layout and etc., can we do some evaluations on this ? for example how hard is it to translate cars to ships ? ) 
3. People have observed that for diffusion models, certain sampling decisions result in similarly structured pictuers (e.g. random seed), are such decisions already considered ? 
clarity,_quality,_novelty_and_reproducibility: Clarity: High

Quality: High and easy to follow

Novelty: Limited

Reproducibility: High
summary_of_the_review: The paper applies diffusion model to image translation and showed some good translation examples. The method is very straightfoward though and quantitative results are a bit mixing : A->B translation shows superior peromance but not B->A. Even though the results are interesting to see but the analysis could go a bit deeper: for example the translated image does not repsect preservation of details, translation is only good between very closely related domains and etc.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: The paper propose an image translation method based on diffusion models for preserving privacy of domain data. Theoretical analyses explain the efficacy of the method. Experimental results demonstrate the utility of the proposed method in a wide variety of translation tasks.
strength_and_weaknesses: The authors perform a lot of theoretical analyses of the proposed DDIBs from an optimal transport viewpoint. However, the actual implementation of DDIBs remains elusive. The detail comments are as follows.
1. How to train a DDIBs model? It is mentioned that two diffusion models are used in image encoding and decoding. How to train these two diffusion models? What is the loss function?
2. The authors state "Our method is limited in its application to  color transfer, as one model is required for each image, which demands significant compute for mass  experiments." For the color transfer task, why one model is required for each image?
3. Too few comparison methods are used in the experiments. Except for Section 4.2, there is no comparison method in the rest of the experiment. The paper presents only several quantitative experimental results. Moreover, the provided quantitative results is not enough to show the superiority of the proposed method. All this makes the experimental part less convincing.
4. In abstract, the full name is not given when ODE first appears.
clarity,_quality,_novelty_and_reproducibility: The paper is overall well-organized. The theoretical analyses are somewhat sufficient. But the innovation of the proposed DDIBs seems unclear.
summary_of_the_review: The motivation for this paper is interesting, but the implementation details of the method are unclear and the experiment part is not convincing.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: The paper proposes to use two independently trained diffusion models on different image domains. The source diffusion models encodes the the source image to a latent domain and then it is decoded to the target domain using the target diffusion model. 
The paper shows that the continuous diffusion resulting from applying successively these diffusion models is cycle consistent.  
The code was provided during  the rebuttal period, making the work reproducible. The cycle consistency of the proposed method being a feature by design of the method makes it an appealing method for translation between unpaired domains. 


justification_for_why_not_higher_score: The work is not novel as it builds on diffusion models but it provides a nice application in cycle consistent generation. 
justification_for_why_not_lower_score: The paper presents a good idea for cycle consistent generation and it is backed up with thorough experimentation and is reproducible.
Decision: Accept: poster