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 focuses on the few-shot image generation, especially when there might be a huge gap between the source and target domains. By setting up a valiation pipelline, authors find that existing approaches perform worse than the directly fine-tuning when the source and target domains are apart. In order to tackling such challenging setting, the proposed method leverages Fisher informance to measure the importance on the target domain and performs the corresponding modulation. Experimental results demonstrate the effectiveness of the proposed approache, especially across the relatively challenging domains: \eg from human faces to animal faces. 
strengths_and_weaknesses: - This paper is well-organized and easy to follow.
- The focus on relatively unrelated domains is very interesting and challenging as existing methods just fail under this. 
- Anlysis of evaluating previous approaches is also well-built to uncover the issues.
- Experimental results demonstrate the effectiveness of the proposed adaptation method that explicitly considers the target domain. 
questions: Major concerns:
- As CDC evaluates its approach on skteches as well, I am wondering how well the proposed method performs under such drawing style changes. Ideally, there might be only several kernels affecting the drawing style when adapting from face to sketch faces. Thus, evaluating the proposed method under such style changes is very necessary. 
-  How much the proximity between the source and the target could be relaxed. As the main focus is to challenge the domains that are apart to some extent, what if we fine-tune a face model on other objects like cars? Are all kernels useless? That is, the limit across challenging domains of the proposed method is worth to explore. 
- How many images of target domains are required to determine the importance of kernels. As all experiments are conducted with 10-shot samples, what if we continue to decrease the number of training samples, like 5, even 1 image? And what the FID becomes if we continue to involve more target images, (maybe involving all AFHQ) as Fig.6 does (but more than 200 images). 
- The technical contributions could be better clarified, compared to EWC. It seems that this paper measures the importance on the target domain while EWC does it on the source domain. 

Overall, the exploration on such challenging domains is very necessary for the future research of few-shot generation. Thus, I will change my score depending on the response. 




limitations: The limitations of the proposed method could be discussed more comprehensively.
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: This paper tackle the problem of few-shot image generation, which aims to learn to generate image given a limited number of samples from a domain. The author first conducts a probing analysis of existing methods and finds out the reasons for degenerated performance of these methods under the assumption of relaxed proximity between source and target domains. Secondly, the author propose adaptation-aware kernel modulation to address general FSIG of different source-target domain proximity. This results in good performance, in terms of generated image quality in multiple few-shot settings.
strengths_and_weaknesses: Strengths:

1. The paper is well-written and easy to follow.

2. There are good performance gains. The proposed approach is (to the best of my knowledge) novel, and also theoretically interesting.

3. The model is evaluated on multiple few-shot settings and compared to multiple baselines, which shows its outperformance. The proposed method obtains SOTA on few-shot image generation performance, with a good ablation study.

4. Code is provided, and reproducibility is considered.

Weaknesses:

(Please refer to "Questions".)  
questions: 1. More analysis of the proposed importance probing is recommended. The author proposes important probing to identify the importance of each kernel in the GAN model. To further verify the generalizability of this probing method, I would suggest the authors try the importance probing on different types of pre-trained GAN models. 

2. Is it possible to visualize the importance probing results in Figure 5? In this way, we are able to reveal what kind of information is preserved (w/ high IF) in the modulation, and this can serve as support for the effectiveness of this probing analysis.

3. More discussion on (potential) failure cases is recommended in the main paper or appendix.

Minor comment:

1. The layout of the main paper should be reorganized in the future version. The margins between some figures and tables (e.g., Figure 5 and 6) are too small, making the captions a bit hard to read.  
limitations: Yes, the author discusses the potential societal impact in Section F1 of the appendix.
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: This paper proposes a method to do few-shot (e.g. 10 images) image generation, by transferring a pretrained GAN from a source domain (e.g. FFHQ) to a target domain (e.g. dogs/cats). The method is simple and the empirical performance is good.
strengths_and_weaknesses: Strengths:
1. Few-shot (e.g. 10 images) image generation is practically useful.
2. The empirical performance is good.

Weaknesses:
1. In eq.(3), the size of the fisher information matrix in (3) is square of the number of parameters, i.e., $(c_{in}\cdot k\cdot k)^2$. This could be huge. However, the authors claim this is very lightweight with minimal overhead. Please justify. 
EDIT: I've read the appendix and found the derivations. However, $|\boldsymbol{m}_2|$ seems to mean the number of parameters in $\boldsymbol{m}_2$ (Eq.3 appendix)? But in the text before Eq.3 (main text), $|\boldsymbol{m}_2|=k$, the number of conv channels.
2. The chosen target-domain datasets, babies, metfaces, cat, dog, wild are actually quite similar to the source domain (human or animal faces). According to [33], the domain gaps between human and animal faces is not so big as they look.
3. In Fig. 4 and 6, the authors should include TGAN+ADA as an important baseline, instead of focusing on the much weaker TGAN. According to [33], when there are 1k target-domain images, the FID is quite low (around 25, Fig. 9d [33]), when adapting from FFHQ to Cat. So I expect that at the left side (200 shots) of Fig 6, TGAN+ADA should have competitive performance.
4. Table 2, Fig 5 and Fig 6 are crammed in Page 9.

questions: 1.  I see connections between Fisher information (FI) and class saliency [a] or other similar attribution methods. But these attribution methods use first-order derivatives only, so they are much cheaper. Have you verified that FI is indeed advantageous over first-order attribution methods? In other words, Is using second-order derivatives indeed necessary?
2. In Fig. 2, the Sunglasses dataset is from [32]. But in remaining text, it's from [8]. However, I couldn't find the Sunglasses dataset from either of these two papers.
3. The references [32] and [33] are duplicate.
4. Why the first face (the young lady) of G_s in row 7 is the same as the 9th face of G_s in the last row? 
5. Suggest the authors to use superscripts $\boldsymbol{m}_1^i$ instead of $\boldsymbol{m}_1(i)$ in Eq.(3) for better clarity.

[a] Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, 2014.
limitations: 1. It is natural that a user of FSIG can take few-shot images of a particular person and generate diverse images of them. Have the authors tried this setting? How well does it work? If the performance is good, it could be a security threat to face recognition systems.
review_title: null

Meta-review: metareview: This paper focuses on the few-shot image generation task, which is an interesting and challenging problem. It is well-organized and easy to follow. All the reviewers acknowledge that experimental results demonstrate the effectiveness of the proposed adaptation-aware kernel modulation method. Overall, the meta-reviewer recommends acceptance of the paper.
confidence: Certain
Decision: Accept