Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper proposes a novel efficient approach for domain adaptation by introducing a domain-modulation operation enabling finetuning by optimizing a compact parameter space. This domain modulating operation is inspired by modulation introduced in StyleGAN2. However, in contrast to StyleGAN2, the only trainable parameters in this paper are domain parameters. They also propose regularization loss based on CLIP embeddings in order to improve the diversity of the generator. They also propose a HyperDomainNet which enables multi-domain adaptation of StyleGAN2. Quantitative results on one-shot adaptation show comparable performance even after a significant reduction in the number of trainable parameters. Qualitative results on Text-Based Domain Adaptation and Multi-Domain Adaptation are comparable to those when entire models were fine-tuned.
strengths_and_weaknesses: ##### Strengths
1. Efficient approach for domain adaptation by utilizing a domain-modulation operation, thereby, reducing the learnable parameter space for each domain by 4-5 thousand times.
2. The proposed parameterization led to very minor degradation for StyleGAN-NADA and MindTheGap.
3. The paper addresses the three problems of text-based domain adaptation, one-shot domain adaptation, and multi-domain adaptation.
4. The idea of domain-modulation is inspired by modulation in Style-GAN2, but in this paper, the domain-modulating vector is the only learnable parameter.

##### Weaknesses
1. Lack of quantitative results for Text-Based Domain Adaptation and Multi-Domain Adaptation.
2. Ablation study for multiple loss terms in HyperDomainNet is missing.
3. Since the number of trainable parameters has drastically decreased, there is a limit to the possible domain gap between the source and the target domain (as also pointed out by the authors).
questions: ##### Questions
1. Can the authors report quantitative results for Text-Based Domain Adaptation and Multi-Domain Adaptation as well?
2. For HyperDomainNet, there are multiple components in the final training loss (as shown in Appendix A.2.1). Can you perform ablation studies showing the significance of each component in the final training loss?
3. What is the effect of increasing the dimension of domain parameters d? Does having a larger size of d help in the case of datasets with higher source and target domain gaps? Have the authors conducted any experiments on it?

##### Suggestions
1. From lines 197-206: The explanation for collapsing after several hundred iterations would be more rigorous if some reference work on this is provided.
2. In Table 1, the authors have shown the improvement in results by adding in-domain angle consistency loss, but it is only for one-shot adaptation. But the experiments for quantitative results that show the effectiveness of domain-norm regularization in multi-domain adaptation setting is missing.
3. The authors can report results by increasing the dimension of domain parameters d. This would be particularly interesting for datasets with higher source and target domain gaps.

##### Minor comments and typos
1. Line 83: Typcally --> Typically
2. Line 94: consisitency --> consistency
3. Line 153 & Equation 6: clip_accross --> clip_across (same error in multiple places)
4. Line 175: controled --> controlled
5. Line 211: L --> \mathcal{L}
6. Line 231: during --> the
7. Line 256: fisrt --> first

The overall readability of the paper is good. But some of the sentences can be rewritten for better readability.
limitations: The limitations and potential negative societal impact have been discussed in the paper.
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: The few-shot domain adaptation of GAN generally achieve adaptation to the target domain by fine tuning the generator that trained on source domain with only a few data from target domain. However fine-tuning GAN could be extremely time consuming and resource demanding. Considering the application scenario that the discrepancy among domains is low, fine-tuning the whole network might be redundant and unnecessary. In this work, the authors proposed to incorporate a novel domain-modulation operator to each convolution layers such that the model can be adapted to target domain by optimizing a small domain-specific vector. Further, they developed a hyper-network to predict the domain-specific vector to support adapting to multiple domains simultaneously. They conducted extensive experiments to empirically demonstrate the effectiveness of the proposed method.
strengths_and_weaknesses: Strengths:
- Their framework reduced the number of trainable parameters significantly compared with fine-tuning techniques, as the model weights are adapted to target domain by projecting with a domain-specific vector.
- The proposed HyperDomainNet enable the model to adapt to multiple domains simultaneously by predicting the domain-specific adaptation vector.

Weaknesses:
- The abuse of the math notations and the lack of clarification of the shape of each notation. For instance, in eq(7), it is unclear if $\omega_{ijk}$ is a metric or vector. If my understanding is correct, since $s$ and $d$ are vectors, $\omega$ is supposed to be vectors as well.
- The math description could be more concise and precise such that the main body would have sufficient space to include the training objective, instead of put it as supplementary.
- It is better to include more technique details either in the main body or in supplementary to make it self-contained. 
- The writing could be further polished. There are a lot of grammatical errors, e.g line 130 $n$ is a batch size, line 248, "allows to adapt the generator to ..."

questions: - In the supplementary, eq(13), in order to compute the target-target directional loss, are we going to consider all combinations and sum them up? If this is the case, I think the computational cost will exponentially increase with the number of domains increase. In line 271, "training on potentially arbitrary number of domains" is it realistic to handle a large number of domains, i.e millions of domains? In line 273-274, "fix large number of domains (several hundreds)" I can imagine these would be tens of thousands of target-target combinations, what is the computational cost of this case?
- I assume the motivation of tt-direction loss and domain-norm loss is to avoid the model collapse. However I failed to see why these losses are able to prevent the representations of domains overlap with each other. I think tt-direction loss essentially encourage the representations of text from different domains are aligned with the representations of image from different domains. I don't think it prompt the model push apart domains. I would very much appreciate if the author share their insights on this to help me to understand. 
limitations: A more detailed description of their method and more insightful explanation would make the paper more readable.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: Inspired by AdaIN, this paper proposes a domain modulation technique to greatly reduce the trainable parameters (from 30 millions to about 6000) for domain adaptation of the StyleGAN generators. In addition to only using $L_{direction}$ (or $L_\text{clip-across}$), this paper also introduces a new in-domain angle consistency loss $L_{indomain-angle}$ as a regularization term to improve the diversity of the fine-tuned generator. To address the multi-domain adaptation problem,  the paper proposes a hyper network to predict the domain parameters given an input target image.
strengths_and_weaknesses: The paper is well written and easy to follow. I like the idea of reducing the trainable parameters in fine-tuning and further using a hypernet to predict domain parameters. Compared with previous optimization-based fine-tuning methods, this proposed approach is more suitable for real-time applications.
			
However, my concern is whether the proposed method is actually effective or can achieve similar results to prior works. The visual results presented in the paper are not good enough. As far as I know, the results of MTG should also be better than what is shown in the paper. Why skip the MTG results in the original paper and mark the "MTG+indomain" results as MTG results? Why not add the HyperDomainNet results to the visual comparison in Fig 4?

Another question is about the regularization term proposed in the paper. $L_{indomain-angle}$ is very similar to the $L_\text{clip-within}$ proposed in MTG, except that the cos similarity loss is replaced by the l2 loss. There is no evidence that using l2 loss produces better results.

questions: I think the idea of this paper is good. My main concern is the effectiveness of the proposed method. The paper also lacks fair comparison results. If the author can clarify the questions I asked, I may improve the score.

limitations: Adequate
review_title: null

Meta-review: metareview: This paper presents a domain adaptation technique to finetune a GAN's generator by using a small number of domain modulation parameters. This makes finetuning a pre-trained GAN to different domains very efficient, while not sacrificing upon the generation quality.

The paper received generally positive reviews. There were some concerns about missing experimental results and the authors provided those during the rebuttal phase. There were also some concerns about missing quantitative results for the text-based domain adaptation and multi-domain adaptation. This aspects was also satisfactorily discussed in the rebuttal.

Based on the reviews, and the author response and discussion, and my own reading of the paper, I vote for acceptance. However, the authors are advised to take into account the reviewers' feedback and suggestions to improve the camera-ready version.
confidence: Certain
Decision: Accept