HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks
Anonymous Author(s)
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Abstract
Domain adaptation framework of GANs has achieved great progress in recent yearsas a main successful approach of training contemporary GANs in the case of verylimited training data.
In this work, we signiﬁcantly improve this framework byproposing an extremely compact parameter space for ﬁne-tuning the generator.
We introduce a novel domain-modulation technique that allows to optimize only6 thousand-dimensional vector instead of 30 million weights of StyleGAN2 toadapt to a target domain.
We apply this parameterization to the state-of-art domainadaptation methods and show that it has almost the same expressiveness as thefull parameter space.
Additionally, we propose a new regularization loss thatconsiderably enhances the diversity of the ﬁne-tuned generator.
Inspired by thereduction in the size of the optimizing parameter space we consider the problem ofmulti-domain adaptation of GANs, i.e. setting when the same model can adapt toseveral domains depending on the input query.
We propose the HyperDomainNetthat is a hypernetwork that predicts our parameterization given the target domain.
We empirically conﬁrm that it can successfully learn a number of domains at onceand may even generalize to unseen domains.
Introduction
Contemporary generative adversarial networks (GANs) [5, 11, 12, 10, 1] show remarkable performance in modeling image distributions and have applications in a wide range of computer visiontasks (image enhancement [14, 33], editing [6, 23], image-to-image translation [9, 36, 37], etc.).
However, the training of modern GANs requires thousands of samples that limits its applicabilityonly to domains that are represented by a large set of images.
The mainstream approach to sidestepthis limitation is transfer learning (TL), i.e. ﬁne-tuning the generative model to a domain with fewsamples starting with a pretrained source model.
The standard approach of GAN TL methods is to ﬁne-tune almost all weights of the pretrained model[15, 17, 29, 28, 10, 34, 19, 4, 38].
It can be reasonable in the case when the target domain is veryfar from the source one, e.g. when we adapt the generator pretrained on human faces to the domainof animals or buildings.
However, there is a wide range of cases when the distance between datadomains is not so far.
In particular, the majority of target domains used in works [15, 28, 19, 4, 38]are similar to the source one and differ mainly in texture, style, geometry while keep the same contentlike faces or outdoor scenes.
For such cases it seems redundant to ﬁne-tune all weights of the sourcegenerator.
It was shown in the paper [31] that after transfer learning of the StyleGAN2 [12] to similardomains some parts of the network almost do not change.
This observation motivates us to ﬁnd amore efﬁcient and compact parameter space for domain adaptation of GANs.
In this paper, we propose a novel domain-modulation operation that reduces the parameter spacefor 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 ateach convolution layer.
The dimension of the vector d equals 6 thousand that is 5 thousand timesless than the original weights space of the StyleGAN2.
We apply this parameterization for thestate-of-the-art domain adaptation methods StyleGAN-NADA [4] and MindTheGAP [38].
We showthat 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 a new regularization lossthat improves the diversity of the ﬁne-tuned generator.
Such considerable reduction in the size of the proposed parameterization motivates us to considerthe problem of multi-domain adaptation of GANs, i.e. when the same model can adapt to multipledomains depending on the input query.
Typically, this problem is tackled by previous methods just byﬁne-tuning separate generators for each target domain independently.
In contrast, we propose to traina hyper-network that predicts the vector d for the StyleGAN2 depending on the target domain.
Wecall this network as HyperDomainNet.
Such hyper-network would be impossible to train if we neededto predict all weights of StyleGAN2.
The immediate beneﬁts of multi-domain framework consist ofreducing the training time and the number of trainable parameters because instead of ﬁne-tuning nseparate generators we train one HyperDomainNet to adapt to n domains simultaneously.
Anotheradvantage of this method is that it can generalize to unseen domains if n is sufﬁciently large and weempirically observe this effect.
We provide extensive experiments to empirically conﬁrm the effectiveness of the proposed parameterization and the regularization loss on a wide range of domains.
We illustrate that our parameterizationcan 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 isvalidated qualitatively and quantitatively.
Further, we conduct experiments with the HyperDomainNetand show that it can be successfully trained on a number of target domains simultaneously. Also we
show that it can generalize to a number of diverse unseen domains.
To sum up, our main contributions are
• We reduce the number of trainable parameters for domain adaptation of StyleGAN2 [12]generator by proposing the domain-modulation technique.
Instead of fune-tuning all 30millions weights of StyleGAN2 for each new domain now we can train only 6 thousanddimensional vector.• We introduce a novel regularization loss that considerably improves the diversity of the adapted generator.
• We propose a HyperDomainNet that predicts the parameterization vector for the input domain and allows multi-domain adaptation of GANs.
It shows inspiring generalizationresults on unseen domains.
Related Work
Domain Adaptation of GANs The aim of few-shot domain adaptation of GANs is to learn accurateand diverse distribution of the data represented by only a few images.
The standard approach is toutilize a generator pretrained on source domain and ﬁne-tune it to a target domain.
There are generallytwo different regimes of this task.
The ﬁrst one is when we adapt the generator to completely newdata (e.g. faces ! landscapes, churches, etc.) , and the second regime is when the target domainrelates to the source one (e.g. faces ! sketches, artistic portraits, etc.).
Methods that tackle the ﬁrst regime typically require several hundreds or thousands samples to adaptsuccessfully.
Such setting assumes that the weights of the generator should be changed signiﬁcantlybecause the target domain can be very far from the source.
The paper [10] shows that for distantdomains training from scratch gives comparable results to transfer learning.
It also conﬁrms that forsuch regime there is no point to reduce the parameter space.
Typcally such approaches utilize dataaugmentations [10, 24, 34, 35], or use auxiliary tasks for the discriminator to more accurately ﬁt theavailable data [16, 32], or freeze lower layers of the discriminator to avoid overﬁtting [17].
In the second regime the transfer learning is especially crucial because the pretrained generatoralready contains many information about the target domain.
In this setting the required number ofavailable data can be signiﬁcantly smaller and range from hundreds to several images.
The mainchallenges 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 introducerestrictions on the parameter space [22, 18], mix the weights of the adapted and the source generators[20], utilize a small network to force sampling in special regions of the latent space [28], proposenew regularization terms [15, 25], or apply constrastive learning techniques to enhance cross-domainconsisitency [19].
The state-of-the-art methods [4, 38] leverage supervision from vision-language
CLIP model [21].
StyleGAN-NADA [4] applies it for text-based domain adaptation when we haveno access to images but only to the textual description.
MindTheGap [38] employs CLIP model tofurther signiﬁcantly improve the quality of one-shot domain adaptation.
Constraining Parameter Space for GAN’s Adaptation In the second regime of GAN’s adaptationit is especially important for the generator to leverage the information from the source domain duringadapting to the target one.
The common approach is to introduce some restrictions on the trainableweights to regularize them during ﬁne-tuning.
For example, the work [22] proposes to optimize onlythe singular values of the pretrained weights and apply it for few shot domain adaptation, however thereported results show the limited expressiveness of such parameterization [22].
Another method [18]constrains the parameter space for models with batch normalization (BN) layers such as BigGAN[1] by optimizing only BN statistics during ﬁne-tuning.
While it allows to decrease the numberof trainable parameters, it also considerably reduces the expressiveness of the generator [22, 19].
Other approach is to adaptively choose a subset of layers during optimization at each step as in
StyleGAN-NADA [4].
It helps to stabilize the training, however it does not reduce the parameterspace because each layer can potentially be ﬁne-tuned.
In contrast, the size of our parameterization isless by orders of magnitude than the size of the full parameter space while having the comparableexpressiveness.
Preliminaries
In this work, we focus on StyleGAN generators in the context of domain adaptation.
We consider StyleGAN2 [12] as a base model.
As the state-of-the-art domain adaptation methods we use
StyleGAN-NADA [4] and MindTheGAP [38].
StyleGAN2 The StyleGAN2 generation process consists of several components.
The ﬁrst part is amapping network M ( z ) that takes as an input random vectors z 2 Z from the initial latent space, Zthat is typically normally distributed.
It transforms these vectors z into the intermediate latent space
W .
Each vector w 2 W is further fed into different afﬁne transformations A ( w ) for each layer ofthe generator.
The output of this part forms StyleSpace S [30] that consists of channel-wise styleparameters s =
A ( w ) .
The next part of the generation process is the synthesis network G sys thattakes as an input the constant tensor c and style parameters s at the corresponding layers and producesthe ﬁnal feature maps at different resolutions F = G sys ( c, s ) .
These feature maps move on to thelast part which consists of toRGB layers G tRGB that generate the output image I =
G tRGB ( F ) .
Problem Formulation of Domain Adaptation
The problem of domain adaptation of StyleGAN2can be formulated as follows.
We are given a trained generator G A for the source domain A , andthe target domain B that is represented by the one image I B (one-shot adaptation) or by the textdescription t B (text-guided adaptation).
The aim is to ﬁne-tune the weights ✓ of a new generator G B ✓for the domain B starting from the weights of G A .
The optimization process in the general form is
[Equation]
where L is some loss function, n is a batch size, w 1 , . . .
, w n are random latent codes, { G B ✓ ( w i ) } ni =
1and { G A ( w i ) } ni =1 are batches of images sampled by G B ✓ and G A generators, respectively, and B, Aare domains that are represented by images or text descriptions.
CLIP model CLIP [21] is a vision-language model that is composed of text and image encoders
E T , E I , respectively, that maps their inputs into a joint, multi-modal space of vectors with a unitnorm (this space is often called as CLIP space).
In this space the cosine distance between embeddingsreﬂects the semantic similarity of the corresponding objects.
StyleGAN-NADA StyleGAN-NADA [4] is a pioneering work that utilizes the CLIP model [21] fortext-guided domain adaptation of StyleGAN.
The proposed loss function is
[Equation]
The idea is to align the CLIP-space direction between the source and target images
I ( G B ✓ ( w ) , G A ( w )) with the direction between a pair of source and target text descriptions T ( B, A ) .
So, the overall optimization process has the form
[Equation]
In StyleGAN-NADA method the L B ( ✓ ) loss is optimized only with respect to the weights ✓ of thesynthesis network G Bsys which has 24 million weights.
MindTheGap The MindTheGap method [38] is proposed for a one-shot domain adaptation of
StyleGAN, i.e. the domain B is represented by the single image I B .
In principle StyleGAN-NADAmethod can solve this problem just by replacing the text direction  T ( B, A ) from Equation (2) to animage one
[Equation]
where| A | P
[Equation]
A 2 A [ E I ( I A )] is the mean embedding of the images from domain A .
However, as statedin [38] this leads to an undesirable effect that transferred images lose the initial diversity of domain
A and become too close to the I B image.
So, the key idea of the MindTheGap is to replace the meanembedding from Equation (4) by the embedding of projection I ⇤ A of I B image to A domain obtainedby the GAN inversion method II2S [39]:
[Equation]
So, the MindTheGap uses the modiﬁed L 0 direction loss that is renamed to L clip _ accross
[Equation]
G [Equation] In addition to this idea several new regularizers are introduced that force the generator G B ✓ toreconstruct the I B image from its projection I ⇤ A .
It further stabilizes and improves the quality ofdomain adaption.
Overall, the MindTheGAP loss function L MTG has four terms to optimize G B ✓ .
For more details about each loss please refer to the original paper [38].
Approach
4.1 Domain-Modulation Technique for Domain Adaptation
Our primary goal is to improve the domain adaptation of StyleGAN by exploring an effective andcompact parameter space to use it for ﬁne-tuning G B ✓ .
As we described in Section 3 StyleGAN hasfour components: the mapping network M ( · ) , afﬁne transformations A ( · ) , the synthesis network
G sys ( · , · ) , and toRGB layers G tRGB ( · ) .
It is observed in the paper [31] that the main part of
StyleGAN that is mostly changed during ﬁne-tuning to a target domain is the synthesis network
G sys ( · , · ) .
It is also conﬁrmed by StyleGAN-NADA [4] and MindTheGap [38] methods as they adaptonly the weights of G sys ( · , · ) for the target domain.
So, we aim to ﬁnd an effective way to ﬁne-tune the weights of feature convolutions of G sys ( · , · ) .
In
StyleGAN2 [12] these convolutions utilize modulation/demodulation operations to process the input
[Figure]
tensor and the corresponding style parameters s .
Let us revisit the mechanism of these operations:
[Equation]
[Equation]
where w, w 0 and w 00 are the original, modulated and demodulated weights, respectively, s i is thecomponent of the style parameters s , i and j enumerate input and output channels, respectively.
Theidea behind modulation/demodulation is to replace the standard adaptive instance normalization(AdaIN) [26, 3] to a normalization that is based on the expected statistics of the input feature mapsrather than forcing them explicitly [12].
So, the modulation part is basically an adaptive scalingoperation as in AdaIN that is controled by the style parameters s .
This observation inspires us to usethis technique for the domain adaptation.
The problem of ﬁne-tuning GANs to a new domain is very related to the task of style transfer wherethe goal is also to translate images from the source domain to a new domain with the speciﬁed style.
The contemporary approach to solve this task is to train an image-to-image network which takes thetarget style as an input condition.
The essential ingredient of such methods is the AdaIN that providesan efﬁcient conditioning mechanism.
In particular, it allows to train arbitrary style transfer models[8].
So, it 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 to optimize only a vector d with the same dimension as the style parameterss .
We incorporate this vector into StyleGAN architecture by the additional modulation operationafter the standard one from Equation (7):
[Equation]
(9) where d i is the component of the introduced domain parameters d (see Figure 1a).
So, instead ofoptimizing all weights ✓ of the G sys part we train only the vector d .
We apply these new parameterization to StyleGAN-NADA and MindTheGAP methods, i.e. insteadof optimizing its loss functions wrt ✓ we optimize it wrt d vector
(see Figure 1b)
The dimensionof the vector d equals 6 thousand that is 4 thousand times less than the original weights space ✓ of
G sys ( · , · ) part.
While the proposed parameter space is radically more constrained we observe that ithas the expressiveness comparable with the whole weight space.
4.2 Improving Diversity of CLIP-Guided Domain Adaptation
The CLIP-based domain adaptation methods StyleGAN-NADA and MindTheGap use L direction (or
L clip _ accross ) loss (see Equations (2) and (6)) that was initially introduced to deal with the mode
[Figure]
a
[Figure] U
[Figure]
collapsing problem of the ﬁne-tuned generator [4].
However, we empirically observe that it solvesthe issue only partially.
In particular, it preserves the diversity only at the begining of the ﬁne-tuningprocess and starts collapsing after several hundred iterations.
It is a signiﬁcant problem because forsome domains we need much more iterations to obtain the acceptable quality.
The main cause of such undesirable behaviour of the L direction (the same for L clip _ accross ) loss is 201 that it calculates the CLIP cosine distance between embeddings that do not lie in the CLIP space.
Indeed, the cosine distance is a natural distance for objects that lie on a CLIP sphere but becomesless evident for vectors  T,  I that represent the difference between clip embeddings that no longerlie on a unit sphere.
Therefore, the idea behind the L direction loss may be misleading and in practicewe can observe that it still suffers from mode collapse.
We introduce a new regularizer for improving diversity that calculates the CLIP cosine distance onlybetween clip embeddings.
We called it indomain angle consistency loss and we deﬁne it as follows
[Equation]
[Equation] The idea of L indomain  angle loss is to preserve the CLIP pairwise cosine distances between images 209 before and after domain adaptation.
We observe that this loss signiﬁcantly improves the diversity ofthe generator G Bd compared to the original L direction or L clip _ accross losses.
4.3 Designing the HyperDomainNet for Universal Domain Adaptation
The proposed domain-modulation technique allows us to reduce the number of trainable parameterswhich motivates us to tackle the problem of multi-domain adaption of StyleGAN2.
Our aim is totrain the HyperDomainNet that predicts the domain parameters given the input target domain.
Thisproblem can be formulated as follows.
We are given a trained generator G A for a source domain Aand a number of target domains B 1 , . . .
, B m that can be represented by the single image or the textdescription.
The aim is to learn the HyperDomainNet D ' ( · ) that can predict the domain parametersd B i
= D ' ( B i ) which will be used to obtain the ﬁne-tuned generator G B i
d Bi by the domain-modulationoperation (see Section 4.1).
In this work, we focus on the setting when the target domains B 1 , . . .
, B m are represented by textdescriptions t B 1 , . . .
, t B m .
The HyperDomainNet D ' ( · ) takes as an input the embedding of the textobtained by the CLIP encoder E T ( · ) and outputs the domain parameters
d B i
=
D ' ( E T ( t B i )) .
Thetraining process is described in the Figure 2.
To train the HyperDomainNet D ' ( · ) we use the sum of L direction losses for each target domains.
Inaddition, we introduce L tt  direction loss ("tt" stands for target-target) that is the same as L direction , but we compute it between two target domains instead of target and source.
The idea is to keep awaythe images from different target domains in the CLIP space.
We observe that without L tt  direction 228 loss the HyperDomainNet tends to learn the mixture of domains.
In multi-domain adaptation setting, the regularizer L indomain  angle becomes inefﬁcient because 230 during training batch consists of samples from different domains and the number of images from onedomain can be very small.
Therefore, we introduce an alternative regularization L domain  norm for 232 the HyperDomainNet that constrains the norm of the predicted domain parameters.
To be exact itequals to k D ' ( E T ( t B i ))
1 k 2 .
So, the objective function of the HyperDomainNet consists of L direction , L tt  direction and
L domain  norm losses.
For more detailed description of these losses the overall optimization process, 236 please refer to Appendix A.2.
Experiments
In this section, we provide qualitative and quantitative results of the proposed approaches.
At ﬁrst, weconsider the text-based domain adaptation and show that our parameterization has comparable qualitywith the full one.
Next, we tackle one-shot domain adaptation and conﬁrm the same quantitatively andalso show the importance of the L indomain  angle loss.
Finally, we solve the multi-domain adaptationproblem by the proposed HyperDomainNet, show its generalization ability on unseen domains.
Forthe detailed information about setup of the experiments please refer to Appendix A.1.
Text-Based Domain Adaptation We compare the StyleGAN-NADA [4] method with the proposedparameterization and the original version on a number of diverse domains.
In Figure 3, we see that theexpressiveness of our parameterization is on par with the original StyleGAN-NADA.
We observe thatthe domain-modulation technique allows to adapt the generator to various style and texture changes.
For results on more domains please refer to Appendix A.3.
One-Shot Domain Adaptation In this part, we examine our parameterization and the indomainangle consistency loss by applying them to the MindTheGap [38] method.
We show qualitativeand quantitative results and compare them with other few-shot domain adaptation methods such as
StyleGAN-NADA, TargetCLIP [2] and Cross-correspondence [19] method.
To assess the domainadaptation quality we use the standard metrics FID [7], precision and recall [13].
As a target domainwe take the common benchmark dataset of face sketches [27] that has approximately 300 samples.
We consider the one-shot adaptation setting.
We provide the results in Table 1.
At ﬁsrt, we seethat the MindTheGap with our parameterization shows comparable results with the original versionwhile having less trainable parameters by three orders of magnitude.
Secondly, we examine theeffectiveness of the indomain angle consistency.
We show that it considerably improves FID andprecision metrics for both the original MindTheGap and the one with our parameterization.
The qualitative results are provided in Figure 4 for MindTheGap+indomain, MindTheGap+indomainwith our parameterization ("Ours") and StyleGAN-NADA.
For other methods please see Appendix A.4.
We observe that MindTheGap+indomain and our version shows comparable visualquality and outperform StyleGAN-NADA in terms of diversity and maintaining the similarity to thesource image.
Overall, we demonstrate that our parameterization is applicable to the state-of-the-art methods
StyleGAN-NADA and MindTheGap
and it can be further improved by the indomain angle consistencyloss.
Multi-Domain Adaptation Now we consider the multi-domain adaptation problem.
We apply the
HyperDomainNet in two different scenarios: (i) training on ﬁxed number of domains, (ii) training on
[Table]
potentially arbitrary number of domains.
The ﬁrst scenario is simple, we train the HyperDomainNeton 20 different domains such as "Anime Painting", "Pixar", etc.
(for the full list of domains pleaserefer to Appendix).
The second scheme is more complicated.
We ﬁx large number of domains(several hundreds) and calculate its CLIP embeddings.
During training we sample new embeddingsfrom the convex hull of the initial ones and use them in the optimization process (see Figure 2).
Thistechnique allows us to generalize to unseen domains.
For more details about both scenarios pleaserefer to Appendix A.2.
The results of the HyperDomainNet for both scenarios are provided in Figure 5.
The left part is resultsfor the ﬁrst setting, the right one is results for the unseen domains in the second scheme.
For moredomains and generated images please refer to Appendix A.2.
We see that in the ﬁrst scenario the HyperDomainNet 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 HyperDomainNet is effective.
The results for the second scenario looks promising.
We can observe that the HyperDomainNet haslearnt very diverse domains and shows sensible adaptation results for unseen ones.
Conclusion
We propose a novel domain-modulation technique that allows us to considerably reduce the numberof trainable parameters during domain adaptation of StyleGAN2.
We successfully apply it to the stateof-the-art methods.
In addition, we introduce a new indomain angle consistency loss that improvesthe diversity of the ﬁne-tuned generator.
Finally, equipped with the efﬁcient parameterization wepropose the HyperDomainNet for multi-domain adaptation of StyleGAN2.
We empirically show thatit can be trained to several domains successfully.
And when the number of training domains is largeit can generalize to unseen domains.
The main limitation of our approach is that it is not applicable for the cases when target domains arevery far from the source one.
In such setting, we cannot limit the parameter space, so we should usethe full parameterization.
The potential negative societal impacts of domain adaptation of GANs and generally training of
GANs include different forms of disinformation, e.g. deepfakes of celebrities or senior ofﬁcials, fakeavatars in social platforms.
However, it is the issue of the whole ﬁeld and this work does not amplifythis impact.
[-9 , the weight of the L direction is 1 . 
-]