Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 6: Weak Accept
review_content: Summarize what the paper claims to do/contribute.
* The paper proposes a new image-to-image GAN-based translator that uses attention and a new normalization that learns a proper ratio between instance and layer normalization. Experiments benchmark the new method against multiple prior ones, and on a number of dataset pairs.

Clearly state your decision (accept or reject) with one or two key reasons for this choice.
Weak Accept

* The paper was well-written and the method and contributions are clearly explained.
* There is clear novelty in this paper, even if slightly limited. However, the newly proposed normalization seems to work quite well.
* The results look good, however it is hard to compare methods quantitatively with only few samples. (Nothing that the authors could have done: there are many samples in the supplementary material and results seem consistent.) Qualitative measures like FID and KID should be taken with a grain of salt also. It is a big plus that a user study was conducted! (However, details of how these subjects were selected would be useful)
review_title: Official Blind Review #2

final_score: rating: 8: Accept
review_content: I have read the authors' rebuttal and satisfied with their response. Novelty is a little on the lower side, but thorough writing, results, and insightful comparisons make up for this in my opinion. I have updated my score to 8: Accept.

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This paper proposes an approach to perform image translation called U-GAT-IT. In image translation, the goal is to learn a mapping from images in a source domain to corresponding images in a target domain. Contemporary image translation approaches are able to transfer local texture but struggle to handle shape transfer. To address this concern, the authors introduce an attention mechanism based on CAM [1] and an adaptive normalization layer into a GAN-based image translation framework. Results indicate favorable quantitative and qualitative performance relative to a number of baselines.

Specific contributions include:
* Introduction of a normalization layer called AdaLIN that can interpolate between instance normalization and layer normalization based on the input.
* Introduction of an attention mechanism based on CAM [1] that allows the model to focus on specific parts of the image when either generating or discriminating.
* Collection and release of a selfie-to-anime dataset.
* Release of U-GAT-IT code.
  
In my opinion this paper is borderline, leaning towards weak accept. The experiments are thorough and the paper is well-written. I have concerns about the novelty and significance of the work, but overall the paper feels very close to being a finished piece of work in spite of its (relatively minor) flaws.
  
Strong points of this work include the writing and experiments. The paper is clearly organized and feels polished. It cites many relevant works, giving the reader a sense of the contemporary approaches for image translation. There is a thorough description of model architecture, dataset and tuning parameters in the appendix. In addition, code and the selfie-to-anime dataset have been released by the authors. In terms of experiments, the authors provide many qualitative visualizations comparing the proposed model to baselines on various datasets. Quantitative evaluation includes KID and a perceptual evaluation on human subjects.

Weak points include novelty and significance. The proposed approach combines two ideas already applied to image translation (adaptive normalization [3] and attention [4]).  It therefore synthesizes these ideas into an effective algorithm rather than directly adding something new. It is unclear to me how others can build on top of this work to further advance state-of-the-art in image translation. Are more sophisticated normalization and attention mechanisms truly the key to improving image translation in the future?

Specific comments:
* The formulation of AdaLIN in Equation (1) is vague. The text states "parameters are dynamically computed by a fully connected layer from the attention map", but it's not clear what those parameters are in the equation. Explicitly writing \gamma and \beta as functions of the fully-connected layer and \mu_I, \sigma_I, \mu_L, \sigma_L as the corresponding mean and standard deviation expressions would make things more clear.
* The motivation for using layer normalization was discussed in 2.1.1 but I still do not understand why it is beneficial.
* The term "importance weights" has a specific meaning in the context of Monte Carlo methods. I would suggest choosing a different term here.

Questions for the authors:
* How does U-GAT-IT compare to TransGaGa [2]? One of the stated goals of U-GAT-IT is to better handle shape when performing image translation. TransGaGa has a similar motivation and so I would have liked to see an experimental comparison or at the very least a description of how U-GAT-IT differs. What sorts of shape transfer could U-GAT-IT handle that TransGaGa couldn't and vice versa?
* What are the shortcomings of the model and how could they possibly be addressed? 
  
[1] Zhou, B., Khosla, A., Lapedriza, A., Oliva, A. and Torralba, A., 2016. Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2921-2929).
[2] Wu, W., Cao, K., Li, C., Qian, C. and Loy, C.C., 2019. Transgaga: Geometry-aware unsupervised image-to-image translation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 8012-8021).
[3] Huang, X. and Belongie, S., 2017. Arbitrary style transfer in real-time with adaptive instance normalization. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1501-1510).
[4] Mejjati, Y.A., Richardt, C., Tompkin, J., Cosker, D. and Kim, K.I., 2018. Unsupervised attention-guided image-to-image translation. In Advances in Neural Information Processing Systems (pp. 3693-3703).
review_title: Official Blind Review #1

final_score: rating: 6: Weak Accept
review_content: This paper proposes a new attention mechanism for unsupervised image-to-image translation task. The proposed attention mechanism consists of an attention module and a learnable normalization function. Sufficient experiments and analysis are done on five datasets.  

Pros:
1. The proposed method seems to generalize well to the different datasets with the same network architecture and hyper-parameters compared to previous works. This could benefit other researchers who want to apply the method to other data or tasks.
2. The translated results seem more semantic consistent with the source image compared to other methods, although the sores are not the top on photo2portrait and photo2vangogh. The results also look more pleasing.

Cons:
1. The CAM loss is one of the key components in the proposed method. However, there is only the reference and no detailed description in the paper. More intuitive descriptions are necessary for easy understanding.
2. The local and global discriminators are not explained until the result analysis. It’s a bit confusing when I see the local and global attention maps visualization results. It’s better to mention it in the method section.
3. I wonder why some translations are not done at all in the results without CAM in Figure 2(f). Because without CAM, the framework would be somehow similar to MUNIT or DRIT. I suppose the hyper-parameters are not suitable for this setting.
4. The generator model architecture in Figure 1 is confusing. The adaptive residual blocks only receive the gamma and beta parameters. I suppose that the encoder feature maps are also fed into the adaptive residual blocks.
5. In Figure 3, the comparison of the results using each normalization function is reported. While in my view, the results only using GN in decoder with CAM looks more natural. I wonder why the proposed method only consists of instance norm and layer norm? I suppose the group norm might help with the predefined group.
6. In the ablation study, the CAM is evaluated for generator and discriminator together. I would recommend doing this ablation study for generator and discriminator separately to see if it’s necessary for generator or discriminator.
7. It would be good to see some discussion on the attention mechanism compared with other related works. For example,  [a,b] predict the attention masks for unsupervised I2I, but applies them on the pixel/feature spatial level to keep the semantic consistency.
[a] Unsupervised-Attention-guided-Image-to-Image-Translation. NIPS’18
[a] Exemplar guided unsupervised image-to-image translation with semantic consistency. ICLR’19

My initial rating is above boardline.
review_title: Official Blind Review #3

Meta-review: The paper proposes a new architecture for unsupervised image2image translation.
Following the revision/discussion, all reviewers agree that the proposed ideas are reasonable, well described, convincingly validated, and of clear though limited novelty. Accept.
Decision: Accept (Poster)