HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks
Authors: Aibek Alanov
Vadim Titov
Dmitry P. Vetrov
Venue: NeurIPS 2022 Accept (2022)
Keywords: GAN
StyleGAN
Transfer Learning
Domain Adaptation
CLIP
HyperNetwork

Abstract: Domain adaptation framework of GANs has achieved great progress in recent years as a main successful approach of training contemporary GANs in the case of very limited training data. In this work, we significantly improve this framework by proposing an extremely compact parameter space for fine-tuning the generator. We introduce a novel domain-modulation technique that allows to optimize only 6 thousand-dimensional vector instead of 30 million weights of StyleGAN2 to adapt to a target domain. We apply this parameterization to the state-of-art domain adaptation methods and show that it has almost the same expressiveness as the full parameter space. Additionally, we propose a new regularization loss that considerably enhances the diversity of the fine-tuned generator. Inspired by the reduction in the size of the optimizing parameter space we consider the problem of multi-domain adaptation of GANs, i.e. setting when the same model can adapt to several domains depending on the input query. We propose the HyperDomainNet that is a hypernetwork that predicts our parameterization given the target domain. We empirically confirm that it can successfully learn a number of domains at once and may even generalize to unseen domains. Source code can be found at https://github.com/MACderRu/HyperDomainNet

HyperDomainNet:: Universal Domain Adaptation for Generative Adversarial Networks
Aibek Alanov* HSE University, AIRI
Vadim Titov" MIPT AIRI Moscow, Russia ti tov. vw@phystech..eeec edu
Dmitry Vetrov HSE University, AIRI Moscow, Russia vetrovd@yandex.
Moscow, Russia alanov wwbekkgmail..cccoommomaaaamaaaaa com
Abstract
Domain adaptation framework of GANs has achieved great progress in recent years as main successful approach of training contemporary GANs the case of very limited training data. In this work, we significantly improve this framework by proposing an extremely compact parameter space for fine-tuning the generator. We introduce novel vector instead of 30 million weights of StyleGAN2
technique that allows to optimize only
6 boousand-dimensionn adapt to target domain. We apply this parameterization to the state-of-art domain adaptation methods and show that it has almost the same expressiveness as the full parameter space. Additionally,
propose new regularization loss that considerably enhances the diversity of the fine-tuned generator. Inspired by the reduction in the size of the optimizing parameter space we consider the problem of multi-domain adaptation of GANs, i.e. setting when the same model can adapt to the input query. We propose the HyperDDomiiNNet
several domains depending that is hypernttorrk that predicts our parameterization given the target domain. We empirically confirm that can successfully learn number of domains at once and may even generalize to unseen domains. Source code can be found at this github repository
Introduction
Contemporary generative adversarial networks (GANs) 8 14
13 3 show remarkable perfor- mance in modeling image distributions and have applications in wide range of computer vision tasks (image enhancement [1 42 editing E.I image-to-image im-ee translation 12
4r
etc.).
However, the training of modern GANs requires thousands of samples that limits its applicability only to domains that are represented by a large set images. The mainstream approach to sidestep this limitation is transfer learning (TL), i.e. fine-tuning the generative model to a domain with few samples starting with a pretrained source model.
The standard approach of GAN TL methods is to fine-tune almost all weights of the pretrained model 19 12
can reasonable in the case when the target domain very far from the source one, e.g. when we adapt the generator pretrained on human faces to the domain of animals buildings. However, there is a wide range cases when the distance between data domains is not so far. In particular, the majority of target domains used in works 19 24 6 4g are similar to the source one and differ mainly texture, style, geometry while keep the same content like faces or outdoor scenes. For such cases it seems redundant to fine-tune all weights of the source
38
37
IR
44 24
AN
"Equal contribution t Corresponndig author Artificial Intelligence Research Institute
Moscow Institute of Physics and Technology
36th Conference on Neural Information Processing Systems (NcurIPS 2022).

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generator. It was shown in the paper 40 that after transfer learning of the StyleGAN2 15 to similar domains some parts of the network almost do not change. This observation motivates us to find a more efficient and compact parameter space for domain adaptation of GANs.
In this paper, we propose novel domain- for fine-tuning the StyleGAN2. The idea is to optimize for each target domain only a single vector d. We incorporate this vector into the StyleGAN2 architecture through the modulation operation at each convolution layer. The dimension of the vector d equals 6 thousand that is 5 thousand times less than the original weights space of the StyleGAN2. We apply this parameterization for the state-of-the -frt domain adaptation methods StyleGAN-NADA O and MindTheGAP [Σ]]. We show that it has almost the same expressiveness as the full parameterization while being more lightweight. To further advance the domain adaptation framework of GANs we propose new regularization loss that improves the diversity of the fine-tuned generator.
operation that reduces the parameter space
Such considerable reduction in the size of the proposed parameterization motivates us to consider the problem of multi-domain adaptation of GANs, i.e. when the same model can adapt multiple domains depending on the input query. Typically. this problem is tackled by previous methods just by fine-tuning separate generators for each target domain independently. In contrast, we propose train a hyper-network that predicts the vector d for the StyleGAN2 depending on the target domain. We call this network as HypenDomainNet. Such hyper-network would be impossible to train we needed to predict all weights of StyleGAN2. The immediate benefits of multi-domain framework consist of reducing the training time and the number of trainable parameters because instead of fine-tuning n separate generators we train one HyperDDmaiiNNet to adapt to n domains simultaneously. Another advantage of this method that it can generalize to unseen domains if n is sufficiently large and we empirically observe this effect.
We provide extensive experiments empirically confirm the effectiveness of the proposed parameter- ization and the regularization loss on a wide range of domains. We illustrate that our parameterization can achieve quality comparable with the full parameterization (i.e. when we optimize all weights). The proposed regularization loss significantly improves the diversity of the fine-tuned generator that is validated qualitatively and quantitatively. Further, we conduct experiments with the HeperDomainNNet and show that it can be successfully trained on number of target domains simultancously. Also we show that can generalize to number of diverse unseen domains.
To sum up, our main contributions are
We reduce the number of trainable parameters for domain adaptation of StyleGAN2 15 generator by proposing the domain-modulle modulation technique. Instead of tine-tuning all 30 millions weights of StyleGAN2 for each new domain now we can train only 6 thousand- dimensional vector.
We introduce novel regularization loss that considerably improves the diversity of the adapted generator.
We propose HyperDomainNer that predicts the parameterization vector for the input domain and allows multi-domain adaptation of GANs. It shows inspiring generalization results on unseen domains.
2
Related Work
Domain Adaptation of GANs The aim of few-shot domain adaptation of GANs is to learn accurate and diverse distribution of the data represented by only a few images. The standard approach is to utilize a generator pretrained on source domain and fine-tune it to a target domain. There are generally two different regimes of this task. The first one is when data (c.g. faces + landscapes, churches, etc.), and the second regime is when the target domain relates to the source one (e.g. faces + sketches, artistic portraits, etc.).
adapt the generator to completely new
Methods that tackle the first regime typically require several hundreds or thousands samples to adapt successfully. Such setting assumes that the weights of the generator should be changed significantly because the target domain can be very far from the source. The paper [K3 shows that for distant domains training from scratch gives comparable results to transfer learning. It also confirms that for such regime there is no point to reduce the parameter space. Typcially such approaches utilize data augmentations
K 33 4 5
or use auxiliary tasks for the discriminator to more accurately fit the

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available data 20, 11, or freeze lower layers of the discriminator to avoid overfitting [22] Another standard techniques for the effective training of GANs is to apply different normalization methods to stabilize the training process.
21 16 2
In the second regime the transfer learning is especially crucial because the pretrained generator already contains many information about the target domain. In this setting the required number of available data can be significantly smaller and range from hundreds to several images. The main challenges in the case of such limited data are to avoid over-fitting of the generator and leverage its diversity learned from the source domain. To tackle these challenges existing methods introduce restrictions on the parameter space [29 (23, mix the weights of the adapted and the source generators 2201, utilize a small network to force sampling in special regions of the latent space [37,, propose new regularization terms [99 (34, or apply contrastive learning techniques to enhance cross cross-domain
consistency [24] The ofate-of-the-ard methods 6.. 48 leverage supervision from vision-language
3 applies it for text-based domain adaptation when we have no access to images but only to the textual description. MindThheGap 4 employs CLIP model to further significantly improve the quality of one-shot domain adaptation.
CLIP model 27 StyleGAN-NADA
Constraining Parameter Space for GAN's Adaptation In the second regime of GAN's s adaptation it is especially important for the generator to leverage the information from the source domain during adapting to the target one. The common approach is to introduce some restrictions on the trainable weights to regularize them during fine-tuning For example, the work (29] proposes to optimize only the singular values of the pretrained weights and apply it for few shot domain adaptation, however the reported results show the limited expressiveness of such parameterization [2] Another method [ constrains the parameter space for models with batch normalization (BN) layers such as BigGAN 3 by optimizing only BN statistics during fine-tuning. While it allows to decrease the number of trainable parameters, it also considerably reduces the expressiveness of the generator [29 24 Other approach is adaptively choose subset of layers during optimization each step in StyleGAN-NADA [6. It helps to stabilize the training. however it does not reduce the parameter space because each layer can potentially be fine-tuned. In contrast, the size of parameterization is less by orders of magnitude than the size of the full parameter space while having the comparable expressiveness.
3 Preliminaries
In this work, we focus on StyleGAN generators in the context of domain adaptation. We con- sider StyleGAN2
5
base model. As the sfate-of-the-arr domain adaptation methods we use b and MindTheGAP
StyleGAN-NADA
48
StyleGAN2 The StyleGAN2 generation process consists of several components. The first part is mapping network M(z) that takes as an input random vectors z E 2 from the initial latent space, E that is typically normally distributed. It transforms these vectors into the intermediate latent space W. Each vector w e W is further fed into different affine transformations A(w) for each layer of the generator. The output of this part forms StyleSpace S [3 that consists of channel-wise style parameters A(w). The next part of the generation process is the synthesis network Geys that takes as an input the constant tensor e and style parameters at the corresponding layers and produces the final feature maps at different resolutions F = G scss(c s). These feature maps move on to the last part which consists of toRGB layers GingB that generate the output image GiRGB(F)
Problem Formulation of Domain Adaptation The problem of domain adaptation of StyleGAN2 can be formulated follows. We are given trained generator G for the source domain A, and the target domain B that is represented by the one image 1B (one-shot adaptation) or by the text description f (text-guided adaptation). The aim is to fine-tune the weights of new generator G for the domain B starting from the weights of G.A. The optimization process in the general form
(0) = L((G) (w:)) (G (mo)) G. A) -> min,
where is is some loss function, n is a batch size, Wi,..., Wn, are random latent codes, (G (w;) ) and (w:)) are batches of images sampled by GB and 4 generators, respectively, and B, A are domains that are represented by images or text descriptions.
3

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CLIP model CLIP 27 is Iision-language model that is composed of text and image encoders Er, E,. respectively, that maps their inputs into joint, multi-modal space of vectors with unit norm (this space is often called as CLIP space). In this space the cosine distance between embeddings reflects the semantic similarity of the corresponding objects.
StyleGAN-NADA StyleGAN-NADA G is pioneering work that utilizes the CLIP model 2 for text-guided domain adaptation of StyleGAN. The proposed loss function is
AT A) FT Frr(ta), (w) (w), (w)) Er(G (w) (G))
Edirection (G (w), G (w), B, A) =1 AT (w), GA(W)) AT(B, |AT (1)), GA(w))IAAT(BAA)
(2)
The idea to align the CLIP-space direction between the source and target images AI (w), (w)) with the direction between pair of source and target text descriptions AT(B, A). So, the overall optimization process has the form
n (B( Σ Ldirection (G (wx), G G (w.), A) + min.
(3)
8
In StyleGAN-NADA method the (0) loss optimized only with respect to the weights of the synthesis network G which has 24 million weights.
MindTheGap The MindTheeGap method 4S is proposed for a one-shot domain adaptation of StyleGAN, i.e. the domain B is represented by the single image Tn. In principle StyleGAN-NADA method can solve this problem just by replacing the text direction AT A) from Equation P to an image
A/ A) Er(Ia)
Σ (Er(TA)], I.EA
(4)
TAI
where Σ [La() is the mean embedding of the images from domain A. However, as stated
A A
in P8 this leads to an undesirable effect that transferred images lose the initial diversity of domain and become too close to the JB image. So, the key idea of the MindTheeGap is to replace the mean embedding from Equation 4 by the embedding of projection I of TB image to A domain obtained by the GAN inversion method I125 49
AI"(B,) A) Er(In) ET
(5)
So, the MindTheGap uses the modified L'direction loss that is renamed to aelipoa
AI(G) ())) (B,
Celip_accront (G (w), G (w)) B,) = AI (w)
(6)
In addition to this idea several new regularizers are introduced that force the generator G to reconstruct the TB image from its projection Ι It further stabilizes and improves the quality of domain adaption. Overall, the MindTheGAP loss function LYTGG has four terms to optimize G For more details about each loss please refer to the original paper 48
4 Approach
4.1
Domain-Modulatio Technique for Domain Adaptation
Our primary goal is to improve the domain adaptation of StyleGAN by exploring an effective and compact parameter space to use for fine-tuning G As we described 3 has four components: the mapping network M.),, affine transformations A(.), the synthesis network (, and toRGB layers G.RGn(-). is observed the paper [10 that the main part of
StyleGAN that is mostly changed during fine-tuning target domain is the synthesis network

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Pretrained StyleGAN2
ModulatedConv
Mapping
ModConv
ModConv
(Weighta
Mod
Affine
CLIP LaPeba
Demod
Conv 3x3
ModConv
ModConv
Finetuned StyleGANZ
modaiinmodulation technique
(b) Fine-tuning StyleGAN2 by optimizing the domain vectorr
Figure I: Detailed diagram of proposed method. (a) Revised ModulatedConv block with introduced domain-modulation operation. (b) Fully detailed training process of the domain adaptation with the proposed domain-modullaion technique.
C.ya(r.)) is also confirmed by StyleGAN-NADA 6 and MindTheGap only the weights of -.,)( for the target domain.
4% methods as they adapt
So, we aim to find an effective way to fine-tune the weights of feature convolutions of Sas (s+)). .). In StyleGAN2 [TS] these convolutions utilize modulation/demoouulatie operations to process the input tensor and the corresponding style parameters Let us revisit the mechanism of these operations:
modulation: Wiyk 84 Wiyk:
w
demodulation: w'jj
ijk
(8)
Σ 1.k
w/ iih +
where w, w' and w/ are the original, modulated and demodulated weights, respectively, 8; the component of the style parameters i and enumerate input and output channels, respectively. The idea behind moculltion/deemodulaiio is to replace the standard adaptive instance normalization to normalization that is based
the expected statistics of the input feature maps rather than forcing them explicitly [15]. So, the modulation part is basically an adaptive scaling operation in AdaIN that is controlled by the style parameters This observation inspires us to use this technique for the domain adaptation.
(AdaIN) 35 5
The problem of fine-tuning GANs to a new domain is very related to the task of style transfer where the goal also translate images from the source domain to new domain with the specified style. The contemporary approach to solve this task is to train an image-to-image network which takes the target style as an input condition. The essential ingredient of such methods the AdaIN that provides an efficient conditioning mechanism. In particular, it allows to train arbitrary style transfer models [ل]. So, motivates us to apply the AdaIN technique for adapting GANs to new domains.
We introduce a new domain-modulation operation that reduces the parameter space for fine-tuning StyleGAN2. The idea is optimize only vector with the same dimension as the style parameters We incorporate this vector into StyleGAN architecture by the additional modulation operation after the standard one from Equation (
Comain-modulatto wiyx = d, Wijks
where d; is the component of the introduced domain parameters d (see Figure I.) So, instead of optimizing all weights # of the Gays part we train only the vector d.
We apply these new parameterization to StyleGAN-NADA and MindTheGAP methods, i.e. instead of optimizing its loss functions wrt 0 we optimize wrt d vector (see Figure [b) The dimension of the vector d equals 6 thousand that is 4 thousand times less than the original weights space 0 of (. part. While the proposed parameter space is radically more constrained we observe that has the expressiveness comparable with the whole weight space.
5

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Domain Embeddings
Per Domain offeets
Domain Modulation
Unseen Descriptions
3D render Pixar
Neanderthal
D cuubodlinng
D
Hyper Net
Train Descriptions
Impressionism
text Encoder
www.
ModConvs
D'embeddinng
Pop Art
CLIP-based
Function
Figure 2: Detailed training process of the HyperDDomiiNNe.. On the training phase only reference descriptions are included into CLIP-guided training.
4.2 Improving Diversity of CLIP-Guided Domain Adaptation
The CLIP-based domain adaptation methods StyleGAN-NADA and MindTheGap use Ldirection (or Ceisp (ccip_accross) loss (see Equations (2) and b6) that was initially introduced to deal with the mode collapsing problem of the fine-tuned generator ( However, we empirically observe that solves the issue only partially. In particular, it preserves the diversity only at the beginming of the fine-tuning process and starts collapsing after several hundred iterations. It significant problem because for some domains we need much more iterations to obtain the acceptable quality.
The main cause of such undesirable behaviour of the Ldirection (the same for Colip scipp_cccross loss is that it calculates the CLIP cosine distance between embeddings that do not lie in the CLIP space. Indeed, the cosine distance is natural distance for objects that lie on a CLIP sphere but becomes less evident for vectors AT, A/ that represent the difference between clip embeddings that no longer lie on a unit sphere. Therefore, the idea behind the Ldirection loss may be misleading and in practice we can observe that it still suffers from mode collapse.
We introduce new regularizer for improving diversity that calculates the CLIP cosine distance only between clip embeddings. We called indomain angle consistency loss and we define it as follows
Lindomain-are angle ((G (w;)) ( (w;) (w.)) B)
(10)
Σ (EE(GA (G (Fr(G E1 (
(11)
The idea of Lindomain -angle loss is to preserve the CLIP pairwise cosine distances between images before and after domain adaptation. We observe that this loss significantly improves the diversity of the generator G compared to the original Latiection or Lelip_ losses.
4.3 Designing the Hyper DomainNet for Universal Domain Adaptation
The proposed domain-modulatio modulation technique allows to reduce the number of trainable parameters which motivates us to tackle the problem of multi-domain adaption of StyleGAN2. Our aim is to train the HyperDDomainNet that predicts the domain parameters given the input target domain. This problem can be formulated as follows. We are given a trained generator G for a source domain A and number of target domains . B,, that can be represented by the single image the text description. The aim is to learn the HyperDomainNet (+) that can predict the domain parameters dn. = (B:) which will be used to obtain the fine-tuned generator Gdp, by the domain-modulation operation (see Section
In this work, we focus on the setting when the target domains Bı...., Bm are represented by text descriptions tB, ...., t.... The HopanNDominNeet (-) takes input the embedding of the text obtained by the CLIP encoder E-(-) and outputs the domain parameters d. = Dip (t..t))) The
training process is described in the Figure
To train the DomanNNo D.(-)) we use the sum of Ldirection losses for each target domains. In addition, introduce Lu Li-dirrction loss ("tt" stands for target-target) that is the same Ldirection

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StyleGAN-NADA
Ours
Vora
Paar
Jkye-
The jaber
Neandothall
Figure 3: Comparison with the original StyleGAN-NADA 6 method (left) and its version with our parameterization.
but we compute it between two target domains instead of target and source. The idea is to keep away the images from different target domains in the CLIP space. We observe that without direcciooon loss the HyperDomainNet tends to learn the mixture of domains.
In multi-domain adaptation setting, the regularizer Lindomain- angle becomes inefficient because during training batch consists of samples from different domains and the number of images from one domain can be very small. Therefore, we introduce an alternative regularization Ldomain norm for the HyperDomainNer that constrains the norm of the predicted domain parameters. To exact equals to (Ep((EE(L))) 1||
So. the objective function of the HyperDomaaiNNet consists of Cdirection Lu-direction and Ldomain-nove norm losses. For more detailed description of these losses the overall optimization process, please refer Appendix A.2
Experiments
In this section, we provide qualitative and quantitative results the proposed approaches. At first, we consider the text-based domain adaptation and show that parameterization has comparable quality with the full one. Next, we tackle one-shot domain adaptation and confirm the same quantitatively and also show the importance of the Cindomain-nngl loss. Finally, solve the multi-domain adaptation problem by the proposed HyperDomainNet, show its generalization ability on unseen domains. For the detailed information about setup of the experiments please refer to Appendix A.
Text-Based Domain Adaptation We compare the StyleGAN-NADA 6 method with the proposed
parameterization and the original version on a number diverse domains. In Figure 3 we see that the expressiveness of our parameterization is on par with the original StyleGAN-NADA We observe that the fomain-modulation technique allows to adapt the generator to various style and texture changes. For results more domains please refer to Appendix A.3 We also provide quantitative results for
7

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MIG (+indomain)
OUT
StyleGAN
Fat
Figure 4: Comparison with one-shot domain adaptation methods. Left block is MindThe- Gap+indomain and right block is StyleGAN-NADA 48 Gap+indomain with our parameterization.
The middle block is the MindThe-
this setting in Appendix A.3.3 which show that parameterization has the comparable performance as the full one.
One-Shot Domain Adaptation In this part, we examine our parameterization and the indomain angle consistency loss by applying them to the MindTheGap [48] method. We show qualitative and quantitative results and compare them with other few-shot domain adaptation methods such as
StyleGAN-NADA, TargetCLIP [ and Cross-corved cooss-coreesoodence [24] method, To assess the domain adaptation quality we the standard metrics FID [IO, precision and recall 17 As target domain we take the common benchmark dataset of face sketches [36] that has approximately 300 samples. We consider the one-shot adaptation setting. We provide the results in Table I At fisrt, see that the MindTheGap with our parameterization shows comparable results with the original version while having less trainable parameters by three orders of magnitude. Secondly, effectiveness of the indomain angle consistency. We show that it considerably improves FID and precision metrics for both the original MindTheGap and the one with our parameterization.
examine the
The qualitative results are provided in Figure 4 for MinddTheCaptindomminn MindTheGaa--nndomain with our parameterization ("Ours") and Sty.eGAN-NADA. For other methods please see Ap- pendix A.4 We observe that MindTTheGap+indomain and our version shows comparable visual quality and outperform StyleGAN-NADA in terms of diversity and maintaining the similarity the source image.
Overall, we demonstrate that our parameterization is applicable to the sate-of-theeart methods StyleGAN-NADA and MindTheGap and can be further improved by the indomain angle consistency loss.
Multi-Domain Adaptation Now we consider the multi-domain adaptation problem. We apply the HyperDDominNNtt in two different scenarios: (i) training on fixed number of domains, (ii) training on potentially arbitrary number of domains. The first scenario is simple, we train the HyperDDmainNeet on 20 different domains such "Anime Painting", "Pixar". etc. (for the full list of domains please refer to Appendix A.2.4p. The second scheme is more complicated. We fix large number of domains (several hundreds) and calculate its CLIP embeddings. During training we sample new embeddings from the convex hull of the initial ones and use them in the optimization process (see Figure 2 This technique allows to generalize to unseen domains. For more details about both scenarios please refer to Appendix A.2
The results of the HyperDomaink for both scenarios are provided in Figure The left part is results for the first setting, the right one results for the unseen domains in the second scheme. For more domains and generated images please refer to Appendix A.2 We see that in the first scenario the Hy-

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HeperDemainnett
HyperDeeaiinkes
inference domains from the train list
Inference unseen domain descriptions
Perming
Paining
Bender
Poar
Uctyon
Femando Painiig
Zombie
Neanderthal
Figure 5: Comparison of training setups. The top row represents the real images embedded into StyleGAN2 latent space which latents are then used for HyperDomainNer inference. The left block represents results obtained from text-descriptions presented in the train list. The right block represents results of HyperDomainNet inference on unseen text-descriptions.
perDomainNet shows results comparable to the case when we train separate models for each domain (see Figure 3 It shows that the proposed optimization process for the HyperDomainNett is effective. The results for the second scenario looks promising. We can observe that the NomannDmainNeet has learnt very diverse domains and shows sensible adaptation results for unseen ones.
We also provide an ablation study on the loss terms we use for training of the Hyper DopainDomiiNNet in Appendix A.2.6 It demonstrates quantitatively and qualitatively that the proposed losses are essential for the effective training of the HyperDomainNet in the setting of the multi-domain adaptation problem.
6 Conclusion
We propose novel of trainable parameters during domain adaptation of StyleGAN2. particular, instead of fine-tuning almost all 30 million weights the StyleGAN2 optimize only niousandddimensionall domain vector. We successfully apply this technique to the state-of-the-art text-based and image-based
technique that allows considerably reduce the number
Table 1: Evaluation of one-shot adaptation methods. Results for TargetCLIP, Cross-correspondence and StyleGAN-NADA methods are taken from 48).
Model quality
Model complexity
Model
FID
Precision
Recall
# trainable parameters
TargetCLIP !
199.33
0.000
0.293
30M
Cross-corresponn cross-correspondence
24
158.86
0.001
0
30M
StyleGAN-NADA
124.55
0.118
24M
MindTheGGp
4%
78.35
0.326
0.017
24M
MindTheGap (our param.)
79.83
0.452
0.017
6k
MindTheGGa--indomain
71.46
0.503
0.014
24M
MindThhGGap-indommiin (our param.)
72.71
0.472
0.028
6k

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domain adaptation methods. We show quantitatively and qualitatively that it can achieve the same quality as optimizing all weights of the StyleGAN2.
To deal with the mode collapsing problem of the domain adaptation methods we introduce a new indomain angle consistency loss Lindomain-and -angle that preserves the CLIP pairwise cosine distances between images before and after domain adaptation. We demonstrate that it improves the diversity of the fine-tuned generator both for text-based and one-shot domain adaptation.
We also consider the problem of multi-domain adaptation of StyleGAN2 when we aim to adapt to several domains simultaneously. Before proposed parameterization it was infeasible because should predict all weights of StyleGAN2 for each domain. Thanks to our efficient parameterization we propose HyperDomain that predicts the 6 dimsanndddimeiional domain vector d for the Style- GAN2 given the input domain. We empirically show that can be trained to 20 domains successfully which is the first time when the StyleGAN2 was adapted several domains simultaneously. We also train the Domaineet for the large number of domains (more than two hundred) with applying different augmentations the domain descriptions (see details in Appendix A.2). We demonstrate practice that in such setting the HyperDomainNet can generalize to unseen domains.
Linitations and societal impact The main limitation of approach that it is not applicable for the cases when target domains are very far from the source one. In such setting, we cannot limit the parameter space, so should use the full parameterization.
The potential negative societal impacts domain adaptation of GANs and generally training of GANs include different forms of disinformation, e.g. deepfakes of celebrities senior officials, fake avatars in social platforms. However, is the issue of the whole field and this work does not amplify this impact.
7 Acknowledgments and Disclosure of F Funding
The publication was supported by the grant for research centers in the field of AI provided by the Analytical Center for the Government of the Russian Federation (ACRF) in accordance with the agreement on the provision of subsidies (identifier of the agreement 000000D730321P500000 and the agreement with HSE University No. 70-2021-00139, Additional revenues of the authors for the last three years: laboratory sponsorship by Samsung Research, Samsung Electronics and Huawei Technologies; Institute for Information Transmission Problems, Russian Academy of Science.
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13


Reviews:
[{"review_id": "wh9vp5DKZQZ", "reviewer": "Reviewer_nhQm", "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.", "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.\n", "limitations": "Adequate", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "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.\n\t\t\t\nHowever, 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?\n\nAnother 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.\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "nVbKENjqlNa", "reviewer": "Reviewer_M94K", "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.", "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?\n- 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.", "rating": 5, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n- 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.\n- The proposed HyperDomainNet enable the model to adapt to multiple domains simultaneously by predicting the domain-specific adaptation vector.\n\nWeaknesses:\n- 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.\n- 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.\n- It is better to include more technique details either in the main body or in supplementary to make it self-contained. \n- 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 ...\"\n", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "8SbgKKiNiK", "reviewer": "Reviewer_VBeZ", "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.", "questions": "##### Questions\n1. Can the authors report quantitative results for Text-Based Domain Adaptation and Multi-Domain Adaptation as well?\n2. 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?\n3. 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?\n\n##### Suggestions\n1. From lines 197-206: The explanation for collapsing after several hundred iterations would be more rigorous if some reference work on this is provided.\n2. 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.\n3. 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.\n\n##### Minor comments and typos\n1. Line 83: Typcally --> Typically\n2. Line 94: consisitency --> consistency\n3. Line 153 & Equation 6: clip_accross --> clip_across (same error in multiple places)\n4. Line 175: controled --> controlled\n5. Line 211: L --> \\mathcal{L}\n6. Line 231: during --> the\n7. Line 256: fisrt --> first\n\nThe 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.", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "##### Strengths\n1. 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.\n2. The proposed parameterization led to very minor degradation for StyleGAN-NADA and MindTheGap.\n3. The paper addresses the three problems of text-based domain adaptation, one-shot domain adaptation, and multi-domain adaptation.\n4. 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.\n\n##### Weaknesses\n1. Lack of quantitative results for Text-Based Domain Adaptation and Multi-Domain Adaptation.\n2. Ablation study for multiple loss terms in HyperDomainNet is missing.\n3. 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).", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}]

Author rebuttal:

Decision: Accept