From: ICLR.cc/2023/Conference/Paper3934/Authors
title: Submission Withdrawn by the Authors

From: ICLR.cc/2023/Conference/Program_Chairs
title: Paper Decision
decision: Reject
metareview:_summary,_strengths_and_weaknesses: This paper targets at improving the generalisation performance of face beauty score prediction methods. The key insight is the poor generalization performance is from the inconsistency of beauty assessment across different datasets and persons. Thus this paper proposes to model this as an order prediction problems instead of a score regression problem. Experiments on several datasets are conducted to validate effectiveness of the proposed method. 

Strength
- this work is well motivated. 
- the presentation is clear and the paper is easy to follow.

Weakness
- The proposed method has limited novelty. It is similar to existing uncertainty-aware learning methods. 
- The proposed method does not show significantly stronger generalization performance than existing methods. This work targets at improving the generalization, but its effectiveness is not well supported by the experiments. 
justification_for_why_not_higher_score: This paper got mixed ratings: 3 borderline ratings and 1 reject. The reviewers giving positive ratings do not give very strong evidence for supporting accept. However, the reviewers having negative opinions raise several concerns on the method novelty (comparison with exiting works) and experiments results (the improvement is not significant). The authors do respond to these concerns and the reviewers are not convinced. AC read the submission and has similar concerns.
justification_for_why_not_lower_score: N/A

From: ICLR.cc/2023/Conference/Paper3934/Authors
title: Reply to Reviewer 6B4M
comment: Thanks for your comments again. We will keep improving our work.

The responses to your concerns are listed one by one below.

**1. The citation of prior work is uneven, many old related works are cited but not discussed, while the difference to recent ones is stated but less convincingly.**

Completeness of related workers has been considered an important aspect of evaluating a research paper. Therefore, we reviewed most works on the FBP task, even if they are old. We only compare with the recent methods because they demonstrated better performances than old ones. In this work, our UOL outperforms these recent methods, which indicates that UOL can outperform old ones as well. Therefore, we did not list the comparison with old methods in the paper.

**2. The results are not strong since this method is built to generalize better on databases different from the one used for training but behaves only marginally better than methods built for good results on a single database.**

Our UOL model aims to learn a robust FB order between facial images, the higher PCs on MEBeauty and Hot-Or-Not can demonstrate a better generalization ability. In fact, the PCs of UOL on two datasets are considerably better than those of most competing methods. Please refer to Table 2, the PCs are improved by around 0.05 to 0.15 than the suboptimal methods, which are significant.

From: ICLR.cc/2023/Conference/Paper3934/Reviewer_T8Rk
title: Feedback
comment: Thanks. I have read the authors' responses and my concerns have been tackled.

From: ICLR.cc/2023/Conference/Paper3934/Reviewer_6B4M
title: After rebuttal review
comment: I have read carefully the response provided to my initial concern, as well as other reviewers' opinion and the responses. Overall I am still not convinced that the paper is strong enough to be accepted and furthermore I did no improve my rating. The following issues still persist:
 - citation of prior work is uneven - many old related works cited but not discussed, while difference to recent ones is stated but less convincingly. The argument that learning "different" is not anticipated from learning to rank (in prior work) is not so strong in my view.
 - the results are not strong since this method is built to generalize better on databases different from the one used for training but behaves only marginally better than methods built for good results on a single database

So, the paper is marginally improved on the theoretically part (technical innovation), slightly better on the performance and applied to a niche, lead me to starting conclusion, which is that the paper is not a good fit for ICLR. Anyway the paper has value and if not accepted here, I strongly encourage a rearrangement and re-submission to the next venue.

From: ICLR.cc/2023/Conference/Paper3934/Authors
title: Response to Reviewer 6B4M Part 3
comment: **Q6. The argument based on Thurstone's theory is forced. Mainly, the theory says that when different human opinions evaluate something, there will not be unanimity, but rather a Gaussian distribution of opinions. But also, the central limit theorem says the same thing. Thurstone does not develop or prove the theory for multidimensional processes, nor that VGG features for face beauty should be Gaussian.**

A6: Our UOL uses VGG to encode the mean and variance of the distribution of ratings, other than the distribution itself. We were inspired by Thurston's theory and then designed the UOL.
Moreover, there is a psychological work has extended Thurston's theory to multidimensional spaces: 

A MULTI-DIMENSIONAL ANALOGY TO THURSTONE'S LAW OF COMPARATIVE JUDGMENT.

COHEN, HARVEY SPENCER.   University of Illinois at Urbana-Champaign ProQuest Dissertations Publishing, 1973. 7411986.


**Q7. The databases do not have acceptance from the persons to be made public. Also, the paper does not discuss any concerns about the intrusiveness of the theme and potential harmful application.**

A7: All datasets used in this work are public datasets whose licenses are under applicable law. 

Thanks for your suggestion. We have included following statement into the revision in **green** color:

“The improper use of FBP models might result in an unethical impact. Devising better data forensics approaches could be countermeasures.”

From: ICLR.cc/2023/Conference/Paper3934/Authors
title: Response to Reviewer 6B4M Part 2
comment: **Q5. The evaluation section is not so strong. For SCUT-FBP5500, it appears that one of the five folds was used as a test and the rest as training (for all databases, yet this is not quite explained). Not all cited works report results for all the databases mentioned here. Therefore, I assumed that, for those papers, author-released code has been used (yet this is also not clarified - especially if multiple training or a unique one was used). It seems to me that a unique solution has been used for all databases, and given that most previous works are rather old, the claim of better fitting w.r.t to domain shift is not well argued Also here F.Bougourzi et al 2022 "Deep learning based face beauty prediction via dynamic robust losses and ensemble regression" Knowledge-Based Systems reports slightly better results on SCUT-FBP database.**

A5: We followed previous works and carried out 5-fold cross-validation on the SCUT-FBP5500 dataset, and report the average result of 5 trials. Specifically, each method has five trained versions. For other methods, we listed their results if their papers reported, otherwise, we carried out a 5-fold test on datasets and reported the average result. As this work aims at a better generalization of FBP models, all models were trained on the SCUT-FBP5500 dataset and tested on all datasets for 5 trials. Please refer to section 3.4.1 for the explanation. We also revised the paper with more details.

The inconsistency of FB standards between datasets could be considered as the domain shift. To address this problem, existing methods must learn the invariant from all datasets. Instead, we expect to learn the relative order of FB from a single dataset which has a better generalization across datasets. They are very different. Please refer to the paper on page 2, lines 3-7.

Thanks for recommending the model proposed by F. Bougourzi et al. We retrained their released code, tested it on all datasets, and report the results below, which are also included in the paper. One can see UOL is slightly inferior to their model in terms of PC on the SCUT-FBP5500, but better in terms of MAE and RMSE. 

|   |   SCUT_FBP5500|  |   |
| :-----: | :----: | :----: | :-----: |
|   | PC &uarr; | MAE &darr; | RMSE &darr; |
| F. Bougourzi et al. |**0.9250**| 0.2009 | 0.2650 |
|**Ours**| 0.9240 |**0.1975**|**0.2633**|

The results on MEBeauty and Hot-Or-Not datasets show that F. Bougourzi et al.’ model outperforms our UOL in terms of MAE and RMSE on Hot-Or-Not. But UOL has a considerably better generalization ability, higher PCs, on both datasets. 

|  |  |  |**MEBeauty**|  |  |  |   
| :-----: | :-----:| :----: | :----: | :-----: | :-----:| :-----: |
|  |  | F. Bougourzi et al. |  |  |**Ours**|  |
|  | PC &uarr; | MAE &darr; | RMSE &darr; | PC &uarr; | MAE &darr; | RMSE &darr; |
| Fold 1  | 0.5308 | 0.5633 | 0.6816 | 0.5469 | 0.5292 | 0.6587 |
| Fold 2  | 0.4688 | 0.5846 | 0.7071 | 0.5585 | 0.5223 | 0.6457 |
| Fold 3  | 0.5273 | 0.5621 | 0.6861 | 0.5594 | 0.5204 | 0.6466 |
| Fold 4  | 0.4708 | 0.5886 | 0.7118 | 0.5421 | 0.5266 | 0.6532 |
| Fold 5  | 0.4580 | 0.5780 | 0.6999 | 0.5592 | 0.5169 | 0.6404 |
| Average | 0.4911 | 0.5753 | 0.6973 |**0.5532**|**0.5230**|**0.6489**|


|  |  |  |**Hot-Or-Not**|  |  |  |   
| :-----: | :-----:| :----: | :----: | :-----: | :-----:| :-----: |
|  |  | F. Bougourzi et al. |  |  | Ours |  |
|  | PC &uarr; | MAE &darr; | RMSE &darr; | PC &uarr; | MAE &darr; | RMSE &darr; |
| Fold 1  | 0.3712 | 0.5248 | 0.6598 | 0.4216 | 0.5487 | 0.6825 |
| Fold 2  | 0.3426 | 0.5274 | 0.6666 | 0.4129 | 0.5327 | 0.6683 |
| Fold 3  | 0.3408 | 0.5302 | 0.6685 | 0.4183 | 0.5367 | 0.6744 |
| Fold 4  | 0.3620 | 0.5213 | 0.6616 | 0.3902 | 0.5524 | 0.6919 |
| Fold 5  | 0.3402 | 0.5308 | 0.6704 | 0.3935 | 0.5345 | 0.6724 |
| Average | 0.3513 |**0.5269** |**0.6653**|**0.4073**| 0.5410 | 0.6779 |

From: ICLR.cc/2023/Conference/Paper3934/Authors
title: Response to Reviewer 6B4M Part 1
comment: Thanks for your comment. The responses to the comments are listed one by one below, the revision has been highlighted in **green** color. Because of the character limit, we will divide the response into multiple parts.

**Q1. The paper cites a lot of prior work in the first paragraph, and few have relevance to the topic. I do not see the point of listing many methods without discussing them or using them in comparison, especially old ones.**

A1: Completeness of related workers has been considered an important aspect of evaluating a research paper. Therefore, we reviewed most works on the FBP task, even if they are old.  

We only compare with the recent methods because they demonstrated better performances than old ones. In this work, our UOL outperforms these recent methods, which indicates UOL can outperform old ones as well. Therefore, we did not list the comparison with old methods in the paper. 

**Q2. The review (Laurentini & Bottino, 2014) does not provide evidence that "Facial Beauty (FB) has a great impact on career development, interpersonal relationships, social status and social acceptance". Some of the papers cited there might provide such evidence, but anyway, this is a strong phrase and it should be motivated carefully.**

A2: Laurentini & Bottino's paper has the following description on page 2, lines 45 - 58:

_In any case, from ancient Greek culture to our times, no one denied the strong influence of beauty on human life. A number of recent studies, as well as everyday common experience, show that face and body harmony is extremely important in general social life. In a society that is virtually obsessed by beauty, looking unpleasant or different can deeply affect self-esteem and result in social isolation, depression and serious psychological disorders. Attractive people are also likely to be regarded as better than unattractive people in a broad social sense. For instance, experimental research shows that better-looking candidates are more likely to be hired than equally qualified but less attractive people. Thus, it is not surprising that more money is spent annually in the US on beauty related items or services than on both education and social services._

We think their study claims that FB has a great impact on career development, interpersonal relationships and social status.

**Q3. pg. 1 near the bottom "Even FB scores across different datasets are normalized to the same scale, obvious biases still exist, as shown in Fig. 1 (a)" Figure 1 shows that there is no unanimity while human observers labeled facial beauty. Nothing about the bias. In particular in fig (a) it is not clear to me what one should observe!?**

A3: Actually, three datasets have varied FB score ranges. Figure 1(a) illustrates that even though the scores of the three datasets are normalized to the same range, facial images with the same score still have different FB appearances. “bias” means that the FB appearances are inconsistent. Most volunteers in our experiments ranked the right face as the most beautiful one, and the left face as the second one. 

**Q4. The paper should have discussed different aspects with respect to prior works (Lin et al 2022; F.Bougourzi et al 2022 Knowledge-Based Systems) in more detail. These papers are too close to the proposed method: the first one uses ranking to learn beauty prediction, while the second removes outliers and goes for statistical fit too. Currently, my understanding is that this paper is a combination of those two.**

A4: The methods proposed by Lin et al. and F. Bougourzi et al. are good but different from our UOL. We disagree that our work is a combination of the two. The differences are the following.

Although R3CNN (Lin et al.) considers relative ranking, it aims to train a better regression model. Essentially, R3CNN still learns a one-to-one mapping between a facial image and a FB score. Instead, our UOL learns the relative order between images. Moreover, UOL also learns a relationship "$\approx$" which denotes the FB appearances of two images are indistinguishable. But, Lin et al.'s work has no such a relation.

Similarly, the method proposed by F. Bougourzi et al. also aims to train a better regression model. They apply dynamic loss parameters to minimize the effect of outliers, which is very different from our uncertainty modeling. They use the variance of multiple ratings to compute the ϵ-error metric other than training networks to model the multiple ratings.

From: ICLR.cc/2023/Conference/Paper3934/Authors
title: Response to Reviewer oq6V
comment: Thanks for your comment. The responses to the comments are listed one by one below.


**Q1. The description method should be clearer.**

A1: Can you please specifically point out which part should be clearer? Other three reviewers gave the following comments and consider our writing clear.

Reviewer T8Rk： ‘The writing of this paper is good, and the structure is easy to follow.’

Reviewer t2iM： ‘A clear description of methods and techniques used and well-structured sections.’

Reviewer 6B4M： ‘The main parts of the proposed method are rather clear.’

We appreciate it if you could help us improve the work more specifically.


**Q2. The novelty and scope appear limited and incremental.**

A2: We do appreciate it if you could give us specific suggestions to improve our work. 


**Q3. The comparison with Co-attention (2019) shows that the improvement is limited.**

A3: We agree Co-attention (2019) is a good regression model when it is trained on the SCUT-FBP5500 dataset. But our UOL performs better than it on MAE and RMSE in Table 1. The PC is slightly worse than Co-attention about only 0.002 which is rather marginal. More important, our UOL focuses on learning robust FB order that has a better generalization. Please refer to Table 2, our UOL outperforms Co-attention in terms of PC by about 0.14 on the Hot-Or-Not dataset and 0.05 on the MEBeauty dataset, which is significant.

**Q4. Concern of code.**

A4: **Code will be available once our paper is accepted.**

From: ICLR.cc/2023/Conference/Paper3934/Authors
title: Response to Reviewer t2iM
comment: **Q: It is not clear if the difference in metrics is significant.**

A: Thanks for your comment. We believe the improvement achieved by our UOL is significant. Please refer to Table 1, we can see the differences among other methods are around 0.01 to 0.1 as well. More important, our UOL focuses on learning robust FB order that has a better generalization. Please refer to Table 2, the PCs are improved by around 0.05 to 0.15 than the suboptimal methods, which are significant.

From: ICLR.cc/2023/Conference/Paper3934/Authors
title: Response to Reviewer T8Rk
comment: Thanks for your comment. The responses to the comments are listed one by one below.

**Q1. The use of uncertainty is very similar to previous uncertainty-related methods.**

A1: Thanks for raising this point. We have included the comparison with these methods in the revision in **blue** color. Probout [1] replaces the intermediate activations with low-dimensional Gaussian distributions by adjusting the activation function, and obtains uncertainty in a lightweight manner instead of traditional Bayesian approaches. GP-DNNOR [2] considers the image quality as a distribution rather than a feature point, then models the uncertainty by a low-dimensional Gaussian distribution. However, low-dimensional Gaussian distribution naturally limits the feature expressiveness. DUL [3] and POEs [4] also model facial images as multi-dimensional Gaussian distributions, but the uncertainty is introduced to reduce the effect of the inherent noise in the image on the learning of data embedding, so only the "mean" features are used in the inferential phase. Also, the information bottleneck loss in POEs only ensures that the covariance matrix of distribution is close to the Identity matrix I. Instead, the uncertainty in our UOL is the inconsistency of human cognition, which is different from the Identity matrix. So, we expect that the FB distribution to be close to the discriminal dispersion of human rating, and then take the variance of multiple ratings as the uncertainty to constrain the distribution. Both our motivation and modeling approach have differences from other methods. Actually, we knew these works but excluded them from the paper due to the 9-page limit. 

**Q2. The FP appearance of three images in Figure 1 (a).**

A2: It is an interesting question. In our experiment, many volunteers ranked the right face as the most beautiful one, and the left face as the second one. The inconsistency between you and our volunteers is an example of the inconsistency of human cognition pointed out in this work.

**Q3. It seems that the provided images in Figure 5 have many noises.**

A3: Can you please specify what the “noises” are? The signal noise in the image, or the label (FB score) noises? If it is the signal noise, all images in this study come from datasets without any modification. If it is the label noise, the FB scores are also from datasets.

**Q4. The meaning of the predictions.**

A4: Our UOL model aims to learn a robust FB order between facial images. In FBP tasks, many methods focus on the MAE and RMSE of FB score prediction. In order to compare with these methods, we transferred the FB order to FB scores using the MC rules. Yes, a more accurate prediction of the model indicates a better representation of the trend of certain volunteers. On the contrary, FB order is more general and robust. Thus, UOL demonstrates a better PC in the experiments.



[1]. Gast, J., Roth, S.: Lightweight probabilistic deep networks. In: CVPR. (2018)

[2]. Liu, Y., Wang, F., Kong, A.W.K.: Probabilistic deep ordinal regression based on gaussian processes. In: ICCV. (2019)

[3]. Chang, J., Lan, Z., Cheng, C., Wei, Y.: Data uncertainty learning in face recognition. In: CVPR. (2020)

[4]. Li, W., Huang, X., Lu, J., Feng, J., Zhou, J.: Learning probabilistic ordinal embeddings for uncertainty-aware regression. In: CVPR. (2021)

From: ICLR.cc/2023/Conference/Paper3934/Reviewer_T8Rk
confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
summary_of_the_paper: This paper aims to address problems of the inconsistency of Face Beauty (FB) standards between datasets and the inconsistency of human cognition of FB. To this end, an Uncertainty-oriented Order Learning (UOL) method is proposed, which learns the relative relations and models uncertainty. The experimental results show the effectiveness of the proposed method to some extent.
strength_and_weaknesses: # Strength
1. The writing of this paper is good, and the structure is easy to follow.
2. The motivation about introducing uncertainty sounds reasonable.

# Weaknesses
1. The using of the uncertainty is very similar to previous uncertainty related methods, such as 
[1] Data Uncertainty Learning in Face Recognition. EEE Conference on Computer Vision and Pattern Recognition, 2020.
[2] Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification. AAAI Conference on Artificial Intelligence, 2021.
The authors should provide a detailed comparison with these methods (recent uncertainty related works, not only the above two).
 
2. The authors claim that the three images in Figure 1 (a) have similar FB scores but different FB appearances. However, I think the three images do have the same appearance.

3. It seems that the provided images in Figure 5 have many noises. 

4. Obviously different people have different opinions about the beauty of human faces. I am not sure what the meaning of the predictions in this case. I notice that the training dataset is labeled by only 60 volunteers, thus the predictions of the model just represent the trend of those 60 volunteers. 

clarity,_quality,_novelty_and_reproducibility: The quality, clarity and originality of this work are good.
summary_of_the_review: Please see [Strength And Weaknesses].
correctness: 3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.
technical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
empirical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
flag_for_ethics_review: NO.
details_of_ethics_concerns: None
recommendation: 6: marginally above the acceptance threshold

From: ICLR.cc/2023/Conference/Paper3934/Reviewer_t2iM
confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
summary_of_the_paper: Facial beauty prediction has the potential to impact many applications and this work focuses on two important issues faced in this space. inconsistency in standards and annotations is a real issue and is compounded due to human cognition and inconsistency in what we perceive as beauty. The authors propose uncertainty-oriented order learning and the results presented to establish the efficacy of UOL and its ability to generalize well.
strength_and_weaknesses: Strengths: A well-written paper with a good experimental setup. Background on order learning, uncertainty modeling along with supporting overview figures were well put together. No major issues in the overall work.

Weaknesses: 
The proposed model does report higher PC and lower MAE and RMSE. However, the difference is a few points (around 0.01 to 0.1 in many instances) and so it is not clear if this difference is significant.  It would be beneficial to understand if such a diff is statistically significant. 

clarity,_quality,_novelty_and_reproducibility: A clear description of methods and techniques used and well-structured sections. Code to be made public as mentioned by authors which will definitely help test the reproducibility.


summary_of_the_review: The paper focuses on an important issue around inconsistency around scores and the use of order learning and modeling uncertainty around this.
correctness: 3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.
technical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
empirical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
flag_for_ethics_review: NO.
recommendation: 6: marginally above the acceptance threshold

From: ICLR.cc/2023/Conference/Paper3934/Reviewer_oq6V
confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
summary_of_the_paper: The authors present a method called Uncertainty-oriented Order Learning (UOL) for facial beauty prediction. The  experiments on the FBP benchmark dataset SCUT-FBP5500, Color FERET, Hot-Or-Not, MEBeauty and MIFS show the improvements achieved by their method for facial prediction.
strength_and_weaknesses: Strength 
- The motivation is very clear.
- The idea of order learning is interesting.
- Ablation studies are done to show the effectiveness of the order learning and the losses. 


Weaknesses
- The description method should be more clear.
- The novelty and scope appear limited and incremental. 
- The proposed method in this paper is not well-supported by experiments. The comparison with Co-attention (2008) shows that the improvement is limited. This section needs more discussion.

clarity,_quality,_novelty_and_reproducibility: The novelty and scope appear limited. The code is not available. It seems to me very difficult te reproduce the results.
summary_of_the_review: The novelty and scope appear limited.  Discussions about the important difference between the proposed method and Co-attention would make the paper more convincing.
correctness: 4: All of the claims and statements are well-supported and correct.
technical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
empirical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
flag_for_ethics_review: NO.
recommendation: 5: marginally below the acceptance threshold

From: ICLR.cc/2023/Conference/Paper3934/Reviewer_6B4M
confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
summary_of_the_paper: The paper proposes a method for facial beauty prediction using so called Uncertainty-oriented Order Learning (UOL), to address the inconsistency of Facial Beauty annotation. The method is evaluated on 5 databases. The method uses Thurstone's theory as a psychological foundation.
strength_and_weaknesses: Pro:
- The paper tries to built the proposed method on psychological research
- Evaluation is carried on 5 databases with good results.
- Most of the appendixes are informative 

Cons:
 - (minor) The paper cites a lot of prior work in the first paragraph and few have relevance for the topic. I do not see the point of listing many methods without discussing them or using them in comparison, especially old ones.
 - (medium) The review  (Laurentini & Bottino, 2014) does not provide evidence that "Facial Beauty (FB) has a great impact on career development, interpersonal relationships, social status and social acceptance". Some of the papers cited there might provide such evidence, but anyway this is a strong phrase and it should be motivated carefully
- (medium) pg 1 near bottom "Even FB scores across different datasets are normalized to the same scale, obvious biases still exist, as shown in Fig. 1 (a)" Figure 1 shows that there is no unanimity while human observers labelled facial beauty. Nothing about he bias. In particular in fig (a) it is not clear to me what one should observe!?
- (major) The paper should have discussed different aspects with respect to prior works (Lin et al 2022; F.Bougourzi et al 2022 Knowledge-Based Systems) into more detail. These papers are too close to the proposed method: the first one uses ranking to learns beauty prediction, while the second removes outliers and goes for statistical fit  too. Currently my understanding is that this paper is a combination of those two.
- (major) The argument based on Thurstone's theory is forced. Mainly, the theory says that when different human opinions evaluate something, there will not be unanimity, but rather a Gaussian distribution of opinions. But also the central limit theorem says the same thing. Thurstone does not develop or prove the theory for multidimensional processes, nor that VGG features for face beauty should be Gaussian.  
-  (major) The evaluation section is not so strong. For SCUT-FBP5500, it appears that one of the five folds was used as test and the rest as training (for all databases, yet this is not quite explained). Not all cited works reports results for all the databases mentioned here. Therefore I assumed that, for those paper,  author released code has been used (yet this is also not clarified - especially if multiple training or a unique one was used). It seems to me that a unique solution have been used for all databases  , and given that most previous works are rather old, the claim of better fitting w.r.t to domain shift is not well argued
  Also here F.Bougourzi et al 2022 "Deep learning based face beauty prediction via dynamic robust losses and ensemble regression" Knowledge-Based Systems reports slightly better results on SCUT-FBP database.
clarity,_quality,_novelty_and_reproducibility: Clarity - the main parts of the proposed method are rather clear. The method is reasonable reproducible.
Quality of the writing - it is rather average. The introduction is very long with a lot of citations that are not necessary. Actual prior art in terms of facial beauty prediction is quite weak since the improvement with respect to previous works is not explained properly. The technical motivation of the method is rather weak. Figures (1,4,5) are not explained enough, thus rather unhelpful when trying to follow the method.
Novelty limited - the idea of ranking images in face beauty has been used before (e.g. Lin et al. 3dCnn).  
summary_of_the_review: Overall the method has limited technical novelty, which stems mostly for using known techniques and combining them to a new problem. There are issues with motivation and exposition. The approached them is not central to ICLR, thus potential auditorium is limited. Summed up, it leads to a rather negative result.
correctness: 2: Several of the paper’s claims are incorrect or not well-supported.
technical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
empirical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
flag_for_ethics_review: Yes, Legal compliance (e.g., GDPR, copyright, terms of use)
Yes, Potentially harmful insights, methodologies and applications
Yes, Responsible research practice (e.g., human subjects, data release)
details_of_ethics_concerns: My concerns w.r.t this paper are limited and marginal . While the paper does not introduce any dataset with face beauty prediction thus it does not break directly anything, yet it does not show any concern about the facial data is using. The databases do not have acceptance from the persons to be made public.
Also, the paper does not discuss any concerns about the intrusiveness of the theme  and potential harmful application. I believe that is not "ok" to see one day that your facial image is public and labelled as being "very ugly" by an automatic solution or by a set of observers. The paper argues about using makeup as a way to improve you facial beauty, which is not  unanimously accepted from a social point of view.

Again, the criticism is for the lack of discussion and concerns on ethical perspectives of the proposed method and not for revealing or disclosing something private. 
recommendation: 3: reject, not good enough