SlowGuess/NeurIPS_2023_OCR huggingface dataset:
In this section, we have planned to conduct a user study in one-shot GDA to enhance our evaluation process. Specifically, our user study involves presenting users with a reference image, a source image, and four adapted images from different methods (DORM++, Generalized One-shot Domain Adaption [56], DynaGAN [20], and DiFa [55]). We will ask users to choose the best adapted image for each of three measurements: (i) image quality, (ii) style similarity with the reference, and (iii) attribute consistency with the source image. To ensure statistical significance, we will generate 500 samples for each method and involve 50 users in our study. Each user will be randomly assigned 50 samples from the 500, and they will have unlimited time to complete the evaluation. As shown in Table 7, preliminary results indicate that users strongly favor our DoRM in all three aspects, reflecting the effectiveness of our approach compared to the alternative methods.
Table 7: User study on one-shot GDA. The numbers represent the percentage of users who favor the images synthesized corresponding method among the all four methods.
Model Comparison	image quality	style similarity	attribute consistency
DoRM++ (Ours)	59.02%	69.35%	67.16%
Generalized One-shot Domain Adaption [56]	7.38%	16.33%	11.79%
DynaGAN [20]	1.44%	3.73%	3.41%
DiFa [55]	32.16%	10.59%	18.24%
A.5 Experiments of DoRM and DoRM++ on the one-shot and 10-shot GDA
Figure 15 thoroughly explore the performance of DoRM++ in a 10-shot GDA scenario and that of DoRM in a one-shot GDA context. The results of these experiments illustrate that both DoRM++ and DoRM exhibit strong performance in both few-shot and one-shot GDA scenarios. Notably, DoRM++ showcases enhanced cross-domain consistency compared to DoRM in the context of one-shot GDA.
A.6 Hybrid-domain Generation
In Figure 17, we present the results of generating hybrid domains using our proposed model. Our DoRM has a unique generator structure that is similar to the mechanism of the human brain. This structure endows the DoRM with two novel capabilities: memory and domain association. These capabilities enable the DoRM to not only retain knowledge from previously learned domains when generating images in new domains, but also integrate multiple learned domains and synthesize images in hybrid domains that were not encountered during training.
Additionally, we proceed to provide a comparative analysis of three methods within this section: Our DoRM/DORM++, DynaGAN [20], and Domain Expansion [30]. As illustrated in Figure 18, we strive to alleviate any confusion by conducting comprehensive experiments on the memory and domain association capabilities of these three methods under one-shot and 10-shot settings. These results effectively demonstrate that the baseline methods exhibit poorer performance across various settings compared to our DoRM/DoRM++. Notably, both DynaGAN and Domain Expansion experience difficulties in achieving successful hybrid domain generation, highlighting the distinctive advantages of our approach.
Finally, we meticulously scrutinized the quantitative experiments conducted within the Domain Expansion framework, leading us to adopt a cosine similarity evaluation metric termed "domain similarity" (Sim) based on CLIP image encoder  $(E_I)$ . This metric is employed to provide a quantitative assessment of the fidelity exhibited by hybrid domains. In detail, for given generative images in the hybrid domain "sketch-baby"  $(I_{SB})$ , we extract image features from the provided images  $(E_I(I_{SB}))$  as well as its corresponding target images  $(E_I(I_S)$  and  $E_I(I_B))$ . Therefore, the "domain similarity" to "sktech" and "baby" domain are defined as Sim1 = cos(EI(Isb), EI(Is)) and Sim2 = cos(EI(Isb), EI(IB)), respectively.
Subsequently, we compute the cosine similarity for each case. The quantification of results from both the one-shot and 10-shot experiments is meticulously presented in Table 8 and Table 9, respectively. These outcomes distinctly illustrate the remarkable performance of our proposed DoRM in both single-domain and hybrid-domain generation. It is imperative to highlight a notable observation amidst these findings: a certain outlier exists. Specifically, the images generated by DynaGAN and HyperdomainNet within the "elsa-sunglasses" domain exhibit a remarkably high domain similarity with the "sunglasses" domain, while conversely displaying significantly lower domain similarity with the "elsa" domain. This phenomenon can be rationalized by the generated images closely resembling the "FFHQ" domain, as depicted in Figure 6 of the attached PDF in the rebuttal. Consequently, the domain similarity to the "FFHQ-sunglasses" domain also emerges as notably high. Drawing upon this observation, we emphasize the necessity for a comprehensive evaluation of hybrid-domain generation that seamlessly integrates both qualitative and quantitative results.
A.7 Experiments on Other Source Domains
In addition to the experiments on FFHQ, we conduct other 10-shot generative domain adaptation experiments to qualitatively evaluate the effectiveness of our proposed DoRM approach. Specifically,
we pre-trained a StyleGAN2 on the LSUN-church [52] dataset and adapted the pre-trained GAN to generate haunted house images. The results of our experiments are presented in Figure 19.
Table 8: Cosine similarity (higher is better) of CLIP feature between generated images and corresponding target images on one-shot experiments.
Method	elsa	baby	sunglasses	elsa-baby	elsa-sunglasses
DynaGAN	0.9165	0.7866	0.8882	0.6196/0.7832	0.6245/0.8467
DynaGAN-interpolation	-	-	-	0.6201/0.7836	0.6330/0.8546
HyperdomainNet	0.8740	0.7739	0.8589	0.7007/0.7041	0.6554/0.7882
Domain expansion	0.9075	0.9614	0.9339	0.7022/0.7762	0.7671/0.6177
Ours	0.9309	0.9814	0.9370	0.7173/0.7842	0.7734/0.6377
Table 9: Cosine similarity (higher is better) of CLIP feature between generated images and corresponding target images on 10-shot experiments.
Method	Sketches	Babies	Sunglasses	Sketches-Babies	Sketches-Sunglasses
Domain expansion	0.8958	0.9546	0.9094	0.7480/0.7112	0.7720/0.6735
Ours	0.9492	0.9780	0.9136	0.8809/0.7271	0.8057/0.6973
Sketches
Baby
Amedeo
Figure 9: Training images in 10-shot generative domain adaptation experiments.
Figure 10: 10-shot generative domain Adaptation on FFHQ. We use a source generator pre-trained on the FFHQ [18] dataset and evaluate our proposed DoRM approach on various target domains, including FFHQ-Sunglasses, face caricatures, face paintings by Raphael, face paintings by Amedeo Modigliani, and face paintings by Otto Dix [50]. The training images are shown in Figure 9. Our results demonstrate that our proposed DoRM approach can maintain cross-domain consistency between the source domain and different target domains.
Figure 11: Latent interpolation using the generators adapted to different target domains in 10-shot generative domain adaptation (the first line is the source images). The first and last columns are the generated images with two latent codes after 10-shot generative domain adaptation. The remaining columns are the results by linearly interpolating the two latent codes. According to the figure, all the semantics (e.g., the gender, the haircut and the pose) vary gradually.
Figure 12: Latent edit using the generators adapted to different target domains in 10-shot generative domain adaptation (the first line is the source images). The second and third lines are two popular target domain images, where the first column is the example of 10-shot target images and the other columns are adaptation results.
Figure 13: One-shot GDA in 3D GANs. DoRM for one-shot GDA in EG3D. Left: source image in FFHQ. Right: the generative target domain (Sketch) images.
Figure 14: Three-domain hybrids generation on 10-shot Babies (B), Sunglasses (G), and Sketch (S) datasets. This illustration demonstrates how easily our approach can be adapted to generate hybrids involving more than two domains.
Figure 15: Results of DoRM and DoRM++ on the one-shot and 10-shot GDA.
10-shot
DoRM++
Figure 16: Qualitative comparison on one-shot GDA. The source domain is FFHQ, and the target domains include different reference images, as shown in the first row of the figure. We compare our method with JoJoGAN [6], Generalized One-shot Domain Adaption [56], DynaGAN[20] and DiFa[55]. Our method not only achieves better synthesis quality and diversity but also maintains higher cross-domain consistency than other methods.
Learned Domains
Hybrid Domains
reference
Figure 17: One-shot generative domain adaptation and domain association on FFHQ. The source domain is FFHQ, and the target domains include different reference images, as shown in the first column of the figure. Once our DoRM++ generator learns to synthesize images in multiple target domains, it can integrate the knowledge from the learned multiple domains and synthesize images in hybrid domains which are unseen in the target domains.
FFHQ-Baby
Sketch-Baby
FFHQ-Sunglasses
Sketch-Sunglasses
Ours
10-shot
Figure 18: Comparison with baselines on 1-shot and 10-shot GDA. The outcomes underscore our DoRM's superiority, revealing inferior performance by baseline methods across various scenarios. Remarkably, other methods face challenges in achieving successful hybrid domain generation.
Haunted House
Figure 19: 10-shot generative domain adaptation on LSUN-Church[52]. The source generator is pretrained on LSUN-Church[52]. The target domain is haunted house (10 training images are shown on the left side). The result shows that our method can maintain the cross-domain consistency between the source domain and the target domain.