Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper discovers an interesting phenomenon that the SR network is powerful in discriminating the image degradations instead of image contents, especially for well-trained deep networks with global residual and generative adversarial training. To validate the claim, the authors give some empirical evidence by visualizing the distribution of features via t-SNE. In addition, some inspirable analysis and applications are provided and reported.
strength_and_weaknesses: Strength:

1 Exploring the intrinsic representation of low-level vision models is of great importance to help the design of new methods. This paper makes a step forward towards this goal. 
2 This paper is easy to read, some analysis are inspirable. 

Weakness:

1 The key issue of this paper is how this empirical finding can facilitate the SR task. In common sense, the restoration of textures, especially those regular patterns with strong perceptual prior, relies on the semantic information, or at least the similar patterns that are included in the training set. However, according to the findings in this paper, the model mainly learns the degradation-related information. Is this good or bad for the performance of SR? or in other words if the authors recommend the following researches to keep the global residual and the adversarial loss in the designing of SR network?
2 There lacks sufficient experiments to verify the performance of blind SR with the proposed DDR guidance. Detailed comparison against the state-of-the-art methods should be conducted.

clarity,_quality,_novelty_and_reproducibility: The clarity, quality and novelty is acceptable. 
summary_of_the_review: This paper reports an interesting phenomenon that the SR network shows more discriminative capacity in degradation types rather than semantics. While some analysis are inspirable, the authors fails to validate its effectiveness in facilitating the performance of the SR task.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: This paper aims to analyze the feature representations and explore the "semantics" in SR networks, namely, deep degradation representations. And authors reveal two factors, i.e., adversarial learning and global residual, which influence the extraction of "semantics".
strength_and_weaknesses: [Strength]
1. The paper is well-written and easy to understand.
2. The paper provides comprehensive analyses, demonstrations, and discussions on DDR. The applications to blind SR, distortion identification and generalization evaluation are also interesting.

[Weakness]
1. The main concern is that the issue this paper explores would be trivial for further low-level research and realistic SR applications. There are three aspects that I doubt the significance of this work:
- Empirical settings: All the empirical demonstrations and the important observations and conclusions are mainly based on synthetic data, including artificial blur and noise. Particularly, it is strange that for noise, two version datasets are used for demonstrations, namely, DIV2K-noise20 and DIV2K-noise. For example, in Fig.3, subfigure (a)~(d) has different datasets.
- Although it claims Hollywood100 with real-world old film degradations, it has a stable intrinsic property, indicating film style, image degradation, etc. Even though it presents a different feature distribution from DIV2K-mild/-noise in Fig.3, it is not direct to draw the conclusion that this difference results from the image degradation. I do not think the analyses for DDR or "semantic" are convincing. Besides, why not use a more complex real-world SR dataset, e.g., RealSR, rather than Hollywood100? 
- If ignoring the problems above, it assumes that the observed "semantics" hold. In this case, are those observations and conclusions important for the SR task, especially for real-world SR? The paper has few insights on how to indeed address SR problems. Although there are discussions on blind SR, the paper just conducts the evaluation on synthetic data and shows the learned features. What is the importance of the observation from Fig.11 (in Page 9, "Fig.11 reveals that DDR guidance can make the deep features become more homogenous")? Besides,  the demonstrations in Fig.11 are conducted on Urban100? it is also confusing. Urban100 is not mentioned in main paper.
2. On one hand, the demonstrations and analyses are based on very few SR methods with simple network architectures, for example, not including RCAN with attention mechanism or Transformer based networks. It would be misleading, confusing, and not convincing to understand the contributions of this paper, let alone do further SR research based on this paper. On the other hand, most of the experiments are conducted on synthetic data with artificial image degradation. It is ideal and hand-craft, showing few promising insights on further research. 
clarity,_quality,_novelty_and_reproducibility: The paper has an excellent writing and a clear idea. But I strongly doubt its significance to the related research. 
summary_of_the_review: Most of demonstrations are mainly based on synthetic data. It is not optimistic for its insights to the future work. Even it may bring the misleading to understand the SR research.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper proposes that SR network can learn the distinctive “semantics” named deep degradation representations (DDR). Considering through GAN-based models the SR network can generate more delicate SR textures, the authors believe that there are some kinds of semantic the SR network has learned. Through comprehensively analyzing the feature representations, the authors discover that the semantics in SR network is related to different degradations and two factors are adversarial learning and global residual. Experiments show that adversarial learning and global residual are important and DDR can be applied in many applications and achieves promising results.
strength_and_weaknesses: Pros:
Strength: 
1 The paper is well-written and easy to read. 
2 THis work is well motivated by the observation of applying pretrained SR models to images of different types of degradations. The idea about DDR is very interesting and seems novel to me.
3. Authors conduct extensive experiments to analyze the observation and give reasonable explanations.

Cons:
1 In this work CNN-based models such as SRResNet and SRGAN are adopted to illustrate the DDR generated by SR network. Currently Transformer-based SR network like SwinIR also achieves promising results. Does the observation about DDR also apply to the Transformer-based methods?
2. Do SR models with stronger SR performance perform better in indicating degradation? Can DDR also has the ability to indicate different levels of degradations?
3. In Fig. 3, why are training and test set for CinCGAN and SRGAN chosen to be different? It seems to me that the same setting is more meaningful.
4. In section5, injecting DDR embedding shows better performance, it is better to show some visual results for comparison to illusrate the advantage of DDR guidance. 
5. DDR can indeed indicate the feature difference among the input with different degradations, however, is it reasonable to call DDR a kind of sematics?
clarity,_quality,_novelty_and_reproducibility: This work is well motivated and easy to follow. Extensive experiements are conducted to support their insightful analysis.
summary_of_the_review: Overall, I would like to have this work being accepted because of inspiring observation and insightful analysis.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper analyzes semantics learned in the SR network, and finds that a well trained SR network can naturally extract degradation descriptors. Authors find that conventional or shallow CNN models have difficulty in separating degradation-related semantics. Especially, global residual and adversarial learnings make the SR network extract degradation-related representation. Then, authors exploit their findings for other vision tasks.
strength_and_weaknesses: Strength
- In this paper, interesting findings are introduced such as degradation-related semantics.
- The proposed findings can be adopted for various low-level vision tasks.
- It is interesting that we need to diminish the feature discriminability to improve the model generalization for various degradations. 

Weaknesses
- Although the observation and finding of this paper are interesting, the finding of this paper should be verified by real applications. Although applied to distortion identification and blind SR, it is somewhat insufficient. Comparative analysis with existing blind SR techniques such as IKC is required, and experiments on other degradations such as compressing rather than noise and blur are also required.

- I think that this paper mainly focuses on the SR. However, in the motivation section, experiments for observations are biased to image denoising (or restoration). Also, compared to the traditional method is BM3D which is a representative denoising method. When the reader first sees the paper, it seems strange. Figure 2 should be an experiment on SR. 

- Figure 2 does not show observations for various degradations. Not only noise, but also degradation such as compression, irregular holes, and color tones should be dealt with as well.

- Figure 5~8 seems to be convincing only if more diverse degradations. In addition, it is necessary to verify whether there is a different phenomenon that varies depending on the upsampling factor of the SR network.

- Minor points
    - [1pp] sparse coding(Yang ) → sparse coding (Yang )
    - [3pp] Wang et al. Wang et al. (2020b) → Wang et al. (Wang et al. (2020b))
    - In Figure 2, the results in the last column are from SRCNN? Text above the figure denotes it as SRCNN but texts in the caption denotes as CinCGAN (Yuan et al., 2018) is trained on DIV2K-mild dataset in an unpaired manner.

clarity,_quality,_novelty_and_reproducibility: - This paper is quite clear and has a novel point.

- Detailed Implementation information is somewhat lacking, but overall it is judged that it can be reproduced.

- It has not been verified whether the performance of the real SOTA blind SR model can be improved or whether there are other practical applications. Experiments on distortion identification and blind SR presented in the paper only show that it is applicable to them. It should be demonstrated experimentally whether it can effectively improve the existing distortion identification and blind SR methods.

summary_of_the_review: This paper provides interesting views in the SR network, and analyzes/verifies their assumptions via various experiments. However, experimental validation of applications utilizing degradation-related representations is somewhat lacking. In particular, degradation-related representations should be combined to the recent SOTA  blind SR methods.
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: *Summary*: The paper presents an understanding of Super Resolution networks and their learned representations.  Their main finding is that the super resolution networks learn deep degradation representations (DDR).

*Strengths*: (1) This paper has an interesting finding about SR networks, namely the DDR. The DDR can be applied to many downstream tasks such as blind SR, distortion identification etc. (2) The connection of the feature quality to adversarial learning and global residual is interesting. (3) Good experimental evaluation. The authors also responded to the reviewers and included comparisons to other recent methods like IKC.

*Weaknesses*: (1) The main types of degradation studied in this paper are synthetic and limited to noise based perturbations. (2) The study is limited to only certain architecture types. As reviewers EvNB & CXK4 point out, recent Transformer based architectures are not studied in this work. 
justification_for_why_not_higher_score: The paper studies only limited forms of degradation. I believe a more comprehensive study of architectures and degradation, coupled with real data would make the paper stronger. The key observation in this paper about DDR is interesting and warrants further study.
Decision: Reject