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 presents an idea for a one-shot generative domain adaptation method.  To this end, this paper proposed to minimize both global and local loss. For global loss, this paper exploits the difference between the CLIP embedding of a reference image and the mean embedding of source. Whereas, for local-level constraining, the paper employs attentive style loss.
To validate the idea, the experiments are conducted on multiple pre-trained GAN such as StyleGAN2 trained on FFHQ, StyleGAN-ADA trained AFHQ-Cat. Both the qualitative and quantitative experiments are performed with the baselines.



strengths_and_weaknesses: The paper is generally well written. The paper motivates sufficiently the need of zero-shot generative model and argures the drawbacks of the existing methods.  Also, the related works are discussed adequately and the methods are presented by the equations and diagram. Both the equations and diagram are easy to follow. The experimental results are also convincing to justify their claims.
questions: how diverse were the members of the user study group? On average, how much did they take to complete the assessment? 
 
limitations: One limitation is that this model relies on CLIP embedding, which is trained on a very large corpus.
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: To tackle the one/few-shot generative domain adaptation task, this paper proposes a novel method called DiFa, in which three different losses are introduced to achieve faithful adaptation. Among these three losses, the CLIP loss is computed between the embedding of the reference image and the embedding of the source domain for global-level adaptation. Attentive Style loss is calculated on the intermediate layer of the CLIP image encoder for local-level adaptation. Selective cross-domain consistency loss is introduced to select and retain domain-sharing attributes in the editing latent W+ space to inherit the diversity of the pre-trained generator. Extensive quantitative and qualitative comparisons show the superiority of the proposed method.
strengths_and_weaknesses: Strengths:
- The paper is well written and easy to follow. The framework itself is clear.
- The visualization results demonstrate the proposed method can generate high-quality results on diverse target images, which seems promising to me. The ablation study clearly shows the effect of each loss in the pipeline.

Weaknesses:
- The results for Row 3, 5, and 7 in Figure 5 look weird. Please clarify if an alignment step is used on the target image before domain adaptation. This is somewhat unfair to competing methods if the alignment step is not done.
- The faces generated by DiFa look almost the same except for the face shape. The proposed method appears to have failed to preserve facial identities.

questions: See Weakness, can the authors provide domain adaptation results using aligned target images.
limitations: Adequate
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: The paper studies how to transfer a pre-trained generator from source domain to target domain with only one image available. The main challenges of one-shot adaptation of GANs are (i) generating diverse images and (ii) acquiring domain-specific attributes and styles for target domain. This paper proposes DiFa for diverse generation and faithful adaptation. The key ideas are: (i) aligning the CLIP embedding of target and source images at the global-level and aligning the intermediate token of adapted and reference image with an attentive style loss at the local-level; (ii) introducing selective cross-domain consistency loss to facilitate diverse generation. Experiments results demonstrate the efficiency of the proposed techniques.
strengths_and_weaknesses: - The technical ideas of this paper are simple and easy-to-follow. It is doable to reproduce the experiments results;
- Strong empirical performance shows the significant advantage of the proposed method on one-shot adaptation of GANs, as well as compelling results when extended to latent space editing and zero-shot domain adaptation of GANs;

questions: - The authors claim that the local attributes and visual styles are usually ignored in global-level CLIP adaptation, then why the local level adaptation, which extract the intermediate tokens of reference and adapted image from CLIP image encoder, can faithfully capture the local-level domain-specific characteristics? Is it possible that the domain-specific characteristics obtained from local-level adaptation are not precise?
- Since both of style-mixing trick and the proposed attentive style loss can capture domain-specific styles. What is the main difference between them? Style-mixing methods may lead to visible artifacts when there is a significant shape discrepancy, is there any quantitative evaluation of such shape discrepancy? As the eyeglasses of the third image in the third-to-last row of Figure 5 (a) disappeared, does the eyeglasses attribute belongs to shape discrepancy? 
- The authors claim the two-stage methods find the image corresponding to target image in source domain and treat its CLIP-space embedding as source domain embedding, which may cause the domain gap direction to ignore domain-specific attributes. Is there any experimental or theoretical evidence? In my opinion, such source domain embedding may contain different attributes with adapted images, which may facilitate the diversity.
- How does the proposed method perform when compared to adversarial loss based methods [1] [2] [3]?
- Line 155, “as shown in Fig.4”, should Fig.4 here be Fig.2?

[1] "Few-shot adaptation of generative adversarial networks." arXiv preprint arXiv:2010.11943 (2020).
[2] "One-shot generative domain adaptation." arXiv preprint arXiv:2111.09876 (2021).
[3] "A Closer Look at Few-shot Image Generation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.
limitations: yes
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: The paper proposes a CLIP-based method for generative domain adaptation. The method starts with a (StyleGAN2) generator that is pretrained on a source domain and uses it to train a new version of the generator that can produce images from a target domain, from which only a single reference image is available. During training, a global loss is used to reduce the distance between the CLIP embeddings from the two domains. Furthermore, an attentive style loss is used to capture the local style of the target domain, by aligning intermediate tokens of the CLIP encoder for the generated images and the reference image. Finally, a selective cross-domain consistency loss, that operates in the latent space obtained from an inversion model, is used to preserve the diversity of attributes shared by both domains. The proposed method can also be adapted for use in a zero-shot setting and is compared to other GAN-based domain adaptation methods
strengths_and_weaknesses: ## Strengths:
- The method is well motivated and the paper is clearly written.
- The additional ablation study in figure 10 of the supplementary material gives the reader a good insight into the effect of the α parameter.
- Based on visual assessment and the quantitative evaluation of the method it seems to outperform other similar GAN-based domain adaptation methods.

## Weaknesses:
- The contributions of the paper are not immediately clear from the introduction. The authors should make clear that the novelty in the proposed approach is in the attentive style and the selective cross-domain consistency losses.
- The ablation study is based entirely on a visual assessment of one example of style transfer. The authors should ideally also provide the FID for each version of the model in the ablation study.
- Some points made in the paper require clarification (see Questions section of the review)
questions: - Since the α hyperparameter is proportional to the number of shared attributes assumed between domains, how does its choice vary when the two domains are quite dissimilar.
- The paper claims that FSA has better scores yet suffers from extreme mode collapse. Indeed from visual inspection based on the provided samples. However, FID in theory should correlate not only with perceptual quality but also with diversity. Do the authors have any hypothesis about why the FID of FSA is better in two experiments? 
- The paper mentions that for dissimilar domains (church -> tiger) the end result lacks diversity.  What do such results look like? Is it close to the reference image?  
limitations: Regarding the limitations of the method, the authors mention that for dissimilar domains (church -> tiger) the end result lacks diversity but this would be better understood by the reader if some examples were included in the paper or supplementary material. 

The authors have included a discussion to raise awareness about the potential negative social impact of their work.
review_title: null

Meta-review: metareview: The paper focuses on one-shot generative domain adaption and proposes a novel method to obtain faithful adaptation. It is well written and easy to follow. The visualization results and quantitative evaluations demonstrate the proposed method can generate high-quality results. In all, the meta-reviewer considers the contribution of this paper significant, and worth publication. The authors need to incorporate ethics reviews when preparing the camera ready version.
confidence: Less certain
Decision: Accept