# One-Shot Generative Domain Adaptation

Ceyuan Yang†,1, Yujun Shen†,2, Zhiyi Zhang2, Yinghao Xu1,

Jiapeng Zhu3, Zhirong Wu4, Bolei Zhou1

1The Chinese University of Hong Kong, 2ByteDance Inc.

3Hong Kong University of Science and Technology, 4Microsoft Research Asia

###### Abstract

This work aims at transferring a Generative Adversarial Network (GAN) pre-
trained on one image domain to a new domain referring to as few as just one
target image. The main challenge is that, under limited supervision, it is
extremely difficult to synthesize photo-realistic and highly diverse images,
while acquiring representative characters of the target. Different from
existing approaches that adopt the vanilla fine-tuning strategy, we import two
lightweight modules to the generator and the discriminator respectively.
Concretely, we introduce an attribute adaptor into the generator yet freeze
its original parameters, through which it can reuse the prior knowledge to the
most extent and hence maintain the synthesis quality and diversity. We then
equip the well-learned discriminator backbone with an attribute classifier to
ensure that the generator captures the appropriate characters from the
reference. Furthermore, considering the poor diversity of the training data
(i.e., as few as only one image), we propose to also constrain the diversity
of the generative domain in the training process, alleviating the optimization
difficulty. Our approach brings appealing results under various settings,
substantially surpassing state-of-the-art alternatives, especially in terms of
synthesis diversity. Noticeably, our method works well even with large domain
gaps, and robustly converges within a few minutes for each experiment. 111Code
and models are available at https://genforce.github.io/genda.
$\dagger$ indicates equal contribution.

Figure 1:  Diagram of one-shot generative domain adaptation. Left: The overall
framework, where a GAN model pre-trained on the large-scale source data is
transferred to the target domain with only one training sample. A lightweight
attribute adaptor and attribute classifier are introduced to the frozen
generator and discriminator respectively. Right: Realistic and highly diverse
synthesis results after adapting the pre-trained model with different targets.
Here, our efficient design manages to transfer both semantic attributes and
artistic styles within a few minutes.

##  1 Introduction

Generative Adversarial Network (GAN) (Goodfellow et al., 2014), consisting of
a generator and a discriminator, has significantly advanced image synthesis
yet relies on a large number of training samples (Karras et al., 2018; 2019;
2020b; Brock et al., 2018). Many attempts have been made to train GANs from
scratch with limited data (Zhang & Khoreva, 2019; Zhao et al., 2020d; b;
Karras et al., 2020a; Yang et al., 2021a), but it still requires hundreds or
thousands of images to get a satisfying performance. Sometimes, however, we
may have as few as only one single image as the reference, like the
masterpiece Mona Lisa from Leonardo da Vinci. Under such a case, learning a
generative model with both good quality and high diversity becomes extremely
challenging.

Domain adaptation is a commonly used technique that can apply an algorithm
developed on one data domain to another (Csurka, 2017). Prior works (Wang et
al., 2018; Noguchi & Harada, 2019; Wang et al., 2020; Mo et al., 2020; Zhao et
al., 2020a; Li et al., 2020; Robb et al., 2020) have introduced this technique
to GAN training to alleviate the tough requirement on the data scale.
Typically, they first train a large-scale model in the source domain with
adequate data, and then transfer it to the target domain with only a few
samples. A common practice is to fine-tune both the generator and the
discriminator on the target dataset until the generator produces samples
conforming to the target domain. To stabilize the fine-tuning process and
improve the generation quality and diversity, existing approaches propose to
tune partial parameters (Noguchi & Harada, 2019; Mo et al., 2020; Robb et al.,
2020) and introduce some regularizers (Li et al., 2020; Ojha et al., 2021),
but the overall adaptation strategy remains. When there is only one image from
the target domain, these methods would fell short of synthesis diversity,
producing very similar images.

Recall that the pre-trained model can already produce highly diverse images
from the source domain. It makes us wonder what indeed causes the diversity
drop in the adaptation process. We argue that directly tuning the model
weights will result in the loss of the prior knowledge gained from the large-
scale data. On the other hand, however, when adapting the model to the target
domain, most variation factors (e.g., gender and pose of human faces) could be
reused. These observations help raise a question: is it possible to simply
focus on the most representative characters of the reference image while
inheriting all the other knowledge from the source model?

In this work, we develop a method, called GenDA, for one-shot Generative
Domain Adaptation. In particular, we design a lightweight module connecting
the latent space and the synthesis network. We call this module an attribute
adaptor since it helps adapt the generator with the attributes of the target
image. Unlike the conventional fine-tuning strategy, we freeze the parameters
of the original generator and merely optimize the attribute adaptor during
training. Thereby, we manage to reuse the prior knowledge learned by the
source model and hence inherit the synthesis quality and, more importantly,
diversity. Meanwhile, we employ the discriminator to compete with the
generator via a domain-specific attribute classification. In this way, the
generator is forced to capture the most representative attributes from the
reference, or otherwise, the discriminator would spot the discrepancy.
However, instead of directly tuning the original discriminator, we freeze its
entire backbone and introduce a lightweight attribute classifier on top of
that. Similar to the generator, the discriminator has also learned rich
knowledge in its pre-training. Since the synthesized images before and after
adaptation share most visual concepts (e.g., a face model would still produce
faces after domain transfer), the discriminator can be reused as a well-
learned feature extractor. Therefore, we simply train the attribute classifier
to help guide the generator. Furthermore, since there is only one training
sample (which means no diversity in the target domain), we propose to also
constrain the diversity of the generative domain by truncating the latent
distribution during training. Intuitively, learning a one-to-one mapping would
be easier than learning a many-to-one mapping. Such a design mitigates the
optimization difficulty and further improves the synthesis quality.

We evaluate our approach through extensive experiments on human faces and
outdoor churches. Given only one training image, GenDA can convincingly adapt
the source model to the target domain with sufficiently high quality and
diversity. Such an adaptation includes both attribute-level and style-level,
as shown in Fig. One-Shot Generative Domain Adaptation. Our method outperforms
the state-of-the-art competitors by a large margin both qualitatively and
quantitatively. We also show that, when the number of the samples available in
the target domain increases, GenDA can filter out the individual attributes
and only preserve their common characters (see Fig. 4). Noticeably, GenDA
works well for the extreme cases where there is a large domain gap, like
transferring the characters of Mona Lisa to churches (see Fig. 5). Besides,
thanks to the lightweight design of both the attribute adaptor and the
attribute classifier, GenDA can finish each adaptation experiment within a few
minutes.

##  2 Methodology

The primary goal of this work is to transfer a pre-trained GAN to synthesize
images conforming to a new domain with as few as only one reference image. Due
to the limited supervision, it is challenging to ensure both high quality and
large diversity of the synthesis. Intuitively, according to the rationale of
GANs (i.e., adversarial training between the generator and the discriminator),
the discriminator can easily memorize that only the reference image is real
while all others are fake. In this way, to fool the discriminator, the
generator may have to learn to produce images highly alike the reference,
resulting in a poor synthesis diversity. To mitigate this problem, we propose
a new adaptation algorithm, different from the previous fine-tuning scheme.
Concretely, we (i) interpose an attribute adaptor between the latent space and
the generator to help acquire the most representative characters from the
target image; (ii) augment the discriminator backbone with an attribute
classifier to guide the generator to make appropriate adjustments; and (iii)
propose a diversity-constraint training strategy. Before going into technical
details, we first give some preliminaries of GANs.

###  2.1 Preliminaries

Generative Adversarial Network (GAN) (Goodfellow et al., 2014) is formulated
as a two-player game between a generator and a discriminator. Given a
collection of observed data $\\{{\rm\bf x}_{i}\\}_{i=1}^{N}$ with $N$ samples,
the generator $G(\cdot)$ aims at reproducing the real distribution
$\mathcal{X}$ via randomly sampling latent codes ${\rm\bf z}$ subject to a
pre-defined latent distribution $\mathcal{Z}$. As for the discriminator
$D(\cdot)$, it targets at differentiating the real data ${\rm\bf x}$ and the
synthesized data $G({\rm\bf z})$ as a bi-classification task. These two models
are jointly optimized by competing with each other, as

$\displaystyle{\mathcal{L}}_{G}$ $\displaystyle=-\mathbb{E}_{{\rm\bf
z}\in\mathcal{Z}}[\log(D(G({\rm\bf z})))],$ (1)
$\displaystyle{\mathcal{L}}_{D}$ $\displaystyle=-\mathbb{E}_{{\rm\bf
x}\in\mathcal{X}}[\log(D({\rm\bf x}))]-\mathbb{E}_{{\rm\bf
z}\in\mathcal{Z}}[\log(1-D(G({\rm\bf z})))].$ (2)

After the training converges, the generator is expected to produce images as
realistic as the training set, so that the discriminator cannot distinguish
them anymore.

In this work, we start with a GAN model that is well trained on a source
domain $\mathcal{X}^{src}$, and work on adapting it to a target domain
$\mathcal{X}^{dst}=\\{{\rm\bf x}^{dst}\\}$ that has only one image. In fact,
it is ambiguous to define a “domain” using one image. We hence expect the
model to acquire the most representative characters from the reference image.
Taking face synthesis as an example, the character can include facial
attributes (e.g., age or wearing sunglasses) and artistic styles, as shown in
Fig. One-Shot Generative Domain Adaptation.

###  2.2 One-Shot Generative Domain Adaptation

A common practice to transfer GANs is to simultaneously tune the generator and
the discriminator on the target dataset (Wang et al., 2018). However, as
discussed above, the transferring difficulty increases drastically when given
only one training sample. Existing methods attempt to address this issue by
reducing the number of learnable parameters (Mo et al., 2020; Robb et al.,
2020) and introducing training regularizers (Ojha et al., 2021). Even so, the
overall fine-tuning scheme (i.e., directly tuning $G(\cdot)$ and $D(\cdot)$)
remains and the diversity is low. Differently, we propose a new adaptation
pipeline to preserve the synthesis diversity, which includes an attribute
adaptor, an attribute classifier, and a diversity-constraint training
strategy. Details are introduced as follows.

Attribute Adaptor. Prior works have found that, a well-learned generator is
able to encode rich semantics to produce diverse images (Shen et al., 2020;
Yang et al., 2021b). For instance, a face synthesis model could capture the
variation factors like gender, age, wearing glasses, lighting, etc. Ideally,
this knowledge should be sufficiently reused as much as possible in the target
domain, and then the synthesis diversity can be preserved accordingly. In this
way, the generator focuses on transferring the most distinguishable characters
of the only reference, instead of learning the common variation factors
repeatedly and leading to overfitting due to lack of samples. This helps
improve the data efficiency significantly, which is vital to the one-shot
setting.

According to Xu et al. (2021), the latent code ${\rm\bf z}$ can be viewed as
the generative feature of $G({\rm\bf z})$ that determines the multi-level
attributes of the output image. Motivated by this, we propose to adapt such
features regarding the reference image, yet keep the convolutional kernels
untouched. Concretely, before feeding the latent code ${\rm\bf z}$ to the
generator $G(\cdot)$. we propose to first transform it through a lightweight
attribute adaptor $A(\cdot)$, as

$\displaystyle{\rm\bf z}^{\prime}=A({\rm\bf z})={\rm\bf a}\odot{\rm\bf
z}+{\rm\bf b},$ (3)

where $\odot$ stands for the element-wise multiplication, while ${\rm\bf a}$
and ${\rm\bf b}$ are the learnable weight and bias respectively. With such a
design, the transformed latent code ${\rm\bf z}^{\prime}$ is assumed to carry
the sufficient information of the reference, and therefore $G({\rm\bf
z}^{\prime})$ would conform to the target domain $\mathcal{X}^{dst}$.

Attribute Classifier. Only having the attribute adaptor cannot guarantee the
generator to acquire the representative characters from the training sample.
Following the formulation of GANs, we incorporate the discriminator
$D(\cdot)$, which is also pre-trained on the source domain, to compete with
the generator. In particular, we reuse the backbone $d(\cdot)$ but remove the
last real/fake classification head, and then equip it with a lightweight
attribute classifier $\phi(\cdot)$. Given an image ${\rm\bf x}$, either the
reference image ${\rm\bf x}^{dst}$ or a transferred synthesis $G(A({\rm\bf
z}))$, the classifier outputs a probability of how likely it possesses the
target attribute, as

$\displaystyle p=\phi(d({\rm\bf x})).$ (4)

However, due to the limited supervision provided by one image, the
discriminator can easily memorize the real data, which leads to the
overfitting of discriminator as well as the collapse of the generator (Karras
et al., 2020a). As discussed above, the generated images before and after
domain adaptation are expected to share most variation factors (i.e., a face
model remains to produce faces after adaptation). From this viewpoint, the
knowledge learned by the discriminator in its pre-training could be also
reused. Therefore, unlike existing approaches that fine-tune all or partial
parameters of $D(\cdot)$ (Mo et al., 2020; Wang et al., 2020; Li et al., 2020;
Ojha et al., 2021), we freeze all parameters of $d(\cdot)$ in the entire
training process and merely optimizes $\phi(\cdot)$ to guide $A(\cdot)$ with
adequate adjustments. Regarding the one-shot target domain, the mechanism
behind the classifier is very similar to Exemplar SVM (Malisiewicz et al.,
2011), which also suggests that it is sufficient to obtain a good decision
boundary with one positive and many negative samples. Differently, our
attribute classifier is learned in an adversarial manner through competing
with the attribute adaptor.

Diversity-constraint Strategy. Recall that this work targets at generative
domain adaptation with only one reference image, which means no diversity of
real data. On the contrary, however, the latent code can be sampled randomly
and the pre-trained generator can produce highly diverse images from the
source domain. From this perspective, it might be challenging to match these
two distributions with such a huge diversity gap. To alleviate the
optimization difficulty, we propose a diversity-constraint strategy, which
retains the diversity of the generator during training. Specifically, we
truncate the latent distribution with a strength factor $\beta$, as

$\displaystyle{\rm\bf z}^{\prime}=A(\beta{\rm\bf z}+(1-\beta)\bar{{\rm\bf
z}}),$ (5)

where $\bar{{\rm\bf z}}$ indicates the mean code. Note that, truncation is a
common trick used in the inference of state-of-the-art GANs, like StyleGAN
(Karras et al., 2019) and BigGAN (Brock et al., 2018), to improve synthesis
quality. Nevertheless, to our best knowledge, this is the first time that
truncation is introduced in the training process to preserve the synthesis
diversity.

Full Objective Function. In summary, the adaptor $A(\cdot)$ and the classifier
$\phi(\cdot)$ are trained with

$\displaystyle{\mathcal{L}}_{A}$ $\displaystyle=-\mathbb{E}_{{\rm\bf
z}\in\mathcal{Z}}[\log\big{(}\phi(d(G({\rm\bf z}^{\prime})))\big{)}],$ (6)
$\displaystyle{\mathcal{L}}_{\phi}$ $\displaystyle=-\mathbb{E}_{{\rm\bf
x}\in\mathcal{X}^{src}}[\log\big{(}\phi(d({\rm\bf
x}))\big{)}]-\mathbb{E}_{{\rm\bf
z}\in\mathcal{Z}}[\log\big{(}1-\phi(d(G({\rm\bf z}^{\prime})))\big{)}].$ (7)

##  3 Experiments

We evaluate the proposed method on multiple datasets and settings. In Sec.
3.1, we focus on one-shot generative domain adaptation. Quantitative and
qualitative results indicate that the proposed GenDA can produce much more
diverse and photo-realistic images than previous alternatives. Interestingly,
the shared representative attributes of multiple shots could be also captured
and transferred from the source to the target domain. Sec. 3.2 presents the
quantitative comparison to prior approaches, demonstrating the effectiveness
of our GenDA under the general few-shot setting. Additionally, when there
exists a large domain gap, GenDA can still synthesize reasonable outputs in
Sec. 3.3. Noticeably, the comprehensive ablation studies of each component are
available in Appendix.

Implementation. We choose the state-of-the-art StyleGAN2 (Karras et al.,
2020b) as our base GAN model, following Ojha et al. (2021). StyleGAN2 proposes
a more disentangled $\mathcal{W}$ space in addition to the native latent space
$\mathcal{Z}$. Here, our attribute adaptor is deployed on the $\mathcal{W}$
space. $\bar{{\rm\bf z}}$ in Eq. (5) becomes ${\rm\bf w}_{avg}$, which is a
statistical average in the training of the source generator. To alleviate the
overfitting problem, we apply differentiable augmentation on both the
reference image and the adapted synthesis (Zhao et al., 2020b; Karras et al.,
2020a). In particular, we adopt the augmentation pipeline from StyleGAN2-ADA
(Karras et al., 2020a) and linearly increase the augmentation strength from 0
to 0.6. We use Adam optimizer (Kingma & Ba, 2014) for training and the
learning rates are set as $1.25e^{-4}$ and $2.5e^{-4}$ for $A(\cdot)$ and
$\phi(\cdot)$, respectively. All experiments are stopped when the
discriminator has seen $200K$ real samples. The last “ToRGB” layer (with
$1\times 1$ convolution kernels) of the generator is tuned together with the
adaptor $A(\cdot)$ to make sure aligning the synthesis color space to the
reference image. The hyper-parameter $\beta$ in Eq. (5) is set as 0.7.

Datasets. Our source models are pre-trained on large-scale datasets (e.g.,
FFHQ (Karras et al., 2019) and LSUN Church (Yu et al., 2015)) with resolution
$256\times 256$. We collect target images from FFHQ-babies, FFHQ-sunglasses,
face sketches (Ojha et al. (2021)), and samples from masterpieces (i.e., Mona
Lisa and Van Gogh’s houses) and Artistic-Faces dataset.

Table 1:  Quantitative comparison on one-shot adaptation between FreezeD (Mo
et al., 2020), Cross-Domain (Ojha et al., 2021), and our proposed GenDA.
Evaluation metrics include FID (lower is better), precision (higher means
better quality), and recall (higher means higher diversity). All results are
averaged over 5 training shots.

Methods |  FID |  Prec. |  Recall
---|---|---|---
FreezeD |  147.91 |  0.61 |  0.012
Cross-Domain |  146.74 |  0.53 |  0.000
GenDA (ours) |  80.16 |  0.74 |  0.033
(a) Babies

FID |  Prec. |  Recall |
---|---|---|---
87.32 |  0.93 |  0.000 |
91.92 |  0.80 |  0.000 |
44.96 |  0.76 |  0.067 |
(b) Sunglasses

FID |  Prec. |  Recall
---|---|---
102.11 |  0.52 |  0.009
90.42 |  0.19 |  0.000
87.55 |  0.16 |  0.053
(c) Sketches

Figure 2:  Qualitative comparison on one-shot adaptation between FreezeD (Mo
et al., 2020), Cross-Domain (Ojha et al., 2021), and our proposed GenDA. The
first column shows the reference images. GenDA significantly outperforms the
other competitors from the diversity perspective.  Figure 3:  One-shot
adaptation with different target samples. GenDA manages to capture the
representative characters from the given reference, such as sunglasses,
artistic style for faces, and vegetation, pyramid material for churches.

Evaluation Metrics. Fréchet Inception Distance (FID) (Heusel et al., 2017)
serves as the main metric. Akin to Ojha et al. (2021), we always calculate the
FID between 5,000 generated images and all the validation sets. Additionally,
we report the precision and recall metric (Kynkäänniemi et al., 2019) to
measure the quality and diversity respectively.

###  3.1 One-shot Domain Adaptation

Comparison with Existing Alternatives. For one-shot generative domain
adaptation, we compare against FreezeD (Mo et al., 2020) and Cross-Domain
(Ojha et al., 2021). Noticeably, the implemented FreezeD is further improved
by the data augmentations, which is already a challenging baseline.
Considering the variance brought by different single shots, we calculate all
metrics over 5 training shots. As shown in Tab. 1c, our GenDA remains to
surpass multiple approaches by a clear gap from the perspective of FID.
Besides, to further compare different methods from the views of image quality
and diversity, we also report the precision and recall. Intuitively, a higher
precision means higher image quality (more closer to the real sample), while a
higher recall indicates higher diversity. Although FreezeD (Mo et al., 2020)
and Cross-Domain (Ojha et al., 2021) plausibly achieves better synthesis
quality on sunglasses and sketches, their low recalls strongly imply
overfitting. Fig. 2 confirms that there is insufficient diversity for such two
methods. As a contrast, our GenDA results in the competitive quality and
standing-out diversity from the quantitative and qualitative perspectives. In
particular, GenDA manages to synthesize a man with a cap which is only
available in prior knowledge. Fig. 3 suggests that our GenDA works on
transferring both attributes (sunglasses, gender, vegetation and material) and
artistic styles for face and church model respectively.

Figure 4:  Transferring the “common” semantic of more than one reference
image. On the left are the training samples, while on the top are the samples
synthesized before adaptation. In the remaining rows, images from the same
column are produced with the same latent code. Under the settings of two-shot
adaptation (i.e., the last two rows), we can tell that (1) in the second last
row, all people are wearing eyeglasses (common attribute of the two
references) but they have the same gender (divergent attribute) as the
original synthesis in the top row. (2) similarly, in the bottom row, all
people are female yet the “eyeglasses” attribute is preserved from the
original synthesis.

Common Attributes from Multiple References. There might be a number of
representative variation factors in a given face image (e.g., age, gender,
smile and sunglasses). Therefore, when transferring a pre-trained generative
model on one image, multiple factors could be adapted together. Fig. 4
provides an example of our GenDA on sunglasses. Specifically, we train GenDA
on multiple target domains which might contain a single individual with
different identities or a pair of images. Compared with the source output (the
first row), representative attributes besides sunglasses are also transferred.
For instance, the second and third rows suggest that although all individuals
wear sunglasses, the target’s gender is also adapted to the new synthesis. The
third shot could even affect the gender and hairstyles. This reveals that our
GenDA could capture multiple representative attributes of the target domain.
When the target domain contains more than one shot like the last two rows of
Fig. 4, the representative attributes become the common attributes of all
individuals, leading to the corresponding results (i.e., sunglasses and
gender). Namely, our GenDA is able to capture and adapt the representative
attributes no matter how many images the target domain has.

###  3.2 10-shot Domain Adaptation

As our GenDA could capture the representative attributes precisely with an
increasing number of target shots, we also compare again prior approaches
under the general few-shot adaptation setting. Following the setting of Ojha
et al. (2021), we choose 10-shot target domain for comparison.

Quantitative Comparison. Multiple baselines are introduced to conduct the
quantitative comparisons, including Transferring GANs (TGAN) (Wang et al.,
2018), Batch Statistics Adaptation (BSA) (Noguchi & Harada, 2019), FreezeD (Mo
et al., 2020), MineGAN (Wang et al., 2020), EWC (Li et al., 2020) and Cross-
Domain (Ojha et al., 2021). All these methods adapt a pre-trained source model
to a target domain (e.g., babies, sunglasses and sketches). Akin to Cross-
domain (Ojha et al., 2021), FID serves as the metric for evaluation.

Tab. 2 presents the 10-shot quantitative comparison on several target domains.
Although Cross-Domain (Ojha et al., 2021) has already obtained the competitive
results, our method could still significantly improve the performance by a
clear margin, resulting in the new state-of-the-art synthesis quality on few-
shot adaptation. Specifically, for domains that differ in semantic attributes
(i.e., babies and sunglasses), huge gains are obtained by GenDA. It is also
worth noting that when the domain gap increases (i.e., from realistic facial
images to sketches), the performances of Cross-domain (Ojha et al., 2021) and
FreezeD (Mo et al., 2020) become almost identical (45.67 v.s 46.54) while
substantial improvements of synthesis could remain observed by our method
(31.97). It demonstrates the effectiveness of our method on the general few-
shot settings.

Table 2:  Quantitative comparison on 10-shot adaptation. FID (lower is better) serves as the evaluation metric. Numbers in blue indicate our improvements over the state-of-the-art alternative (Ojha et al., 2021).  10-shot transfer | Babies | Sunglasses | Sketches
---|---|---|---
TGAN (Wang et al., 2018) | 104.79 | 55.61 | 53.41
TGAN+ADA (Karras et al., 2020a) | 102.58 | 53.64 | 66.99
BSA (Noguchi & Harada, 2019) | 140.34 | 76.12 | 69.32
FreezeD (Mo et al., 2020) | 110.92 | 51.29 | 46.54
MineGAN (Wang et al., 2020) | 98.23 | 68.91 | 64.34
EWC (Li et al., 2020) | 87.41 | 59.73 | 71.25
Cross-Domain (Ojha et al., 2021) | 74.39 | 42.13 | 45.67
GenDA (ours) | 47.05 ($-$27.34) | 22.62 ($-$19.51) | 31.97 ($-$13.70)

###  3.3 Cross-Domain Adaptation

In this part, we study the adaptation on unrelated source and target domains.
Specifically, Van Gogh’s houses, Superman and Mona Lisa serve as the target
for a face and church source model respectively. Considering the motivation
that we aim at reusing the prior knowledge (i.e., variation factors) by
freezing the parameters, the synthesis after adaptation is supposed to share
similar visual concepts. That is, a face model would still produce faces no
matter what the target image is. Fig. 5 suggests that the source models remain
to produce the corresponding content. More importantly, the color scheme and
painting styles are also transferred. For example, the red roof at the first
row renders the red glasses, the yellow sky at the second row draws the front
head in yellow. The blue hair, green background and the shadows of Superman
are well adapted to church. The painting style of Mona Lisa is also
transferred.

Obviously, the shared attributes between face and church are quite rare.
Therefore, GenDA pours more attention to the variation factors like color
scheme, texture and painting styles which could be directly transferred across
unrelated domains. From this perspective, our GenDA might enable a new
alternative for neural style transfer task that aims at transferring the
styles of a given image. But more generally, our GenDA is able to transfer
more high-level attributes like gender, age, and sunglasses while the
technique of style transfer might fail, which might be of benefit to art
creation.

Figure 5:  Cross-domain adaptation, where GenDA manages to transfer the
character of an out-of-domain target (first column) to the source domain.

##  4 Related Work and Limitations

Training Generative Models with Limited Data. Many attempts have been taken to
train a generative model on limited data. For one thing, some of the prior
approaches proposed to leverage the data augmentation to prevent the
discriminator from overfitting. Specifically, Zhang & Khoreva (2019)
introduced a type of progressive augmentations. Zhao et al. (2020d)
investigated the effects of various augmentations during the training.
Theoretical analysis was conducted by Tran et al. (2021) for several data
augmentations. Zhao et al. (2020b) proposed to apply the augmentations to both
the real and synthesized images in a differentiable manner. Karras et al.
(2020a) designed an adaptive discriminator augmentation that does not leak to
stabilize the training process. For another, multiple regularizers were also
introduced to provide extra supervision. For instance, Zhao et al. (2020c)
involved the consistency regularization for GANs which shows competitive
performances with limited data. Yang et al. (2021a) incorporated contrastive
learning as an extra task to improve the data efficiency. Karras et al.
(2020a) also pointed out that decreasing the number of parameters of
generators and introducing the dropout (Srivastava et al., 2014) could
alleviate the overfitting problems. Shaham et al. (2019) and Sushko et al.
(2021) designed different frameworks which learn from a single natural image
or video. However, when the number of available images is less than 10, they
usually lead to unsatisfying diversity. Different from these works which learn
from scratch, we focus on the generative domain adaptation, a practical
alternative that first pre-trains a source model on the large-scale dataset
and then transfers it on a target domain with the one-shot image.

Few-shot Generative Domain Adaptation. Generative domain adaptation has
attracted a considerable number of interests due to its practical importance.
Wang et al. (2018) proposed to use the same objective for adaptation. Noguchi
& Harada (2019) fine-tuned the batch statistics merely for the few-shot
adaption. Wang et al. (2020) transformed the original latent space and tuned
the entire parameters for the target domain. Mo et al. (2020) froze the lower-
level representations of the discriminator to prevent overfitting. Zhao et al.
(2020a) revealed that low-level filters of both the generator and
discriminator can be transferred via a new adaptive filter modulation. Li et
al. (2020) penalized certain weights identified by Fisher information. Robb et
al. (2020) learned to adapt the singular values of the pre-trained weights
while freezing the corresponding singular vectors. Ojha et al. (2021) proposed
the cross-domain consistency as a regularization to maintain the diversity.
Different from prior work, we aim at reusing the learned knowledge on the
source domain and thus identify and adapt the representative attributes, which
enables the one-shot domain adaptation and outperforms other alternatives by a
clear margin under the general few-shot settings.

Limitations. Despite the state-of-the-art performances on both one-shot and
few-shot generative domain adaptation, our proposed GenDA still has some
limitations. For example, the rationale behind GenDA is to reuse the prior
knowledge learned by the source GAN model, which hinders it from transferring
a model to a completely different domain. As suggested in Fig. 5, when we
adapt a church model regarding a face image, the outputs are still churches
but not faces. This implies that our method would fail when the inter-subclass
variations are huge. Such a property is a silver lining, depending on the
practical application. A second limitation is that our current design treats
all characters of the reference image as a whole. Taking the first row of Fig.
3 as an example, the sunglasses, skin color, and background are transferred
simultaneously. It is hard to accurately transfer some particular attributes.
However, it is indeed possible to use some auxiliary samples to help define a
common attribute, as shown in Fig. 4. Besides, our GenDA also relies on the
layer-wise stochasticity involved in the generator structure. Concretely, in
our base model, StyleGAN2 (Karras et al., 2020b), the latent code is fed into
all convolutional layers instead of the first layer only. Without the layer-
wise design, the supervision will be hard to back-propagate to the attribute
adaptor given a deep synthesis network. Fortunately, however, such a design is
commonly adopted by the state-of-the-art GANs (Karras et al., 2019; 2020b;
Brock et al., 2018).

##  5 Conclusion

In this work, we propose GenDA for one-shot generative domain adaptation. We
introduce two lightweight modules, i.e., an attribute adaptor and an attribute
classifier, to the fixed generator and discriminator respectively. By
efficiently learning these two modules, we manage to reuse the prior knowledge
and hence enable one-shot transfer with impressively high diversity. Our
method demonstrates substantial improvements over existing baselines in a wide
range of settings.

## Ethics Statement

Training a generative model with limited data is a fundamental and practical
problem. This work significantly advances this field by using as few as only
one image for domain adaptation. Such an improvement is of great importance to
the community. Firstly, it pushes the data efficiency in GAN training to the
limit and demonstrates an early success. We believe more and more methods will
be proposed to keep improving the training efficiency along this direction.
Secondly, our approach is based on reusing the prior knowledge of a pre-
trained GAN model. It provides insights on how to better utilize the pre-
trained generative model for various downstream tasks (like semantic
manipulation and super resolution), which is a widely studied research area.
Thirdly, this work also enables many potential real-world applications, like
data augmentation (to solve the long-tail problem), entertainment, creative
design, artistic imitation, etc. However, every coin has two sides. Our method
considerably reduces the cost of producing fake data, in terms of using less
data, less time, and fewer computing resources. But we believe that its
academic contribution and its positive impact on the industrial algorithm are
more valuable, and that the negative impact will be lowered along with the
development of deep fake detector.

## Reproducibility Statement

The base model used in this work is StyleGAN2 (Karras et al., 2020b), with
official implementation stylegan2-ada-pytorch. The large-scale source datasets
we use are FFHQ and LSUN Church. For one-shot domain adaptation, the reference
image can be easily customized by the users, either from online or from other
datasets, since our approach only requires one training sample. The
implementation and training details are described at the beginning of Sec. 3,
which are shared by all experiments. Besides, all experiments are conducted
with 8 GPUs. The evaluation metrics, including FID (Heusel et al., 2017),
precision and recall (Kynkäänniemi et al., 2019), also follow their official
implementation, FID and improved-precision-and-recall-metric. To ensure the
reproducibility, the code and models will be made publicly available.

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## Appendix

This appendix is organized as follows. Sec. A conducts ablation study on the
proposed approach, including the attribute adaptor, the attribute classifier,
the diversity-constraint strategy, and the hyper-parameter $\beta$ in Eq. (5).
Sec. B analyzes the transformation (i.e., the learnable vector ${\rm\bf a}$ in
Eq. (3)) for attribute adaptation, which provides insights on how our
algorithm works. Sec. C shows how the latent representations are transformed
after adaptation. Sec. D presents the additional comparison with the style
transfer method and investigates the visual relation between and after
adaptation. Sec. E performs latent interpolation with the models adapted on
the target domain, which suggests that our GenDA does not harm the learned
latent structure.

Table A1:  Ablation study on the components proposed in GenDA, including the
attribute adaptor (AA), the attribute classifier (AC), and the diversity-
constraint strategy (DC). Evaluation metrics include FID (lower is better),
precision (higher means better quality), and recall (higher means higher
diversity).

AA |  AC |  DC |  FID |  Prec. |  Recall
---|---|---|---|---|---
|  |  |  109.87 |  0.80 |  0.000
✓ |  |  |  53.59 |  0.57 |  0.064
✓ |  ✓ |  |  50.37 |  0.62 |  0.106
✓ |  ✓ |  ✓ |  47.05 |  0.71 |  0.065
(a) Babies

FID |  Prec. |  Recall
---|---|---
53.06 |  0.72 |  0.003
28.35 |  0.72 |  0.333
25.29 |  0.71 |  0.349
22.62 |  0.78 |  0.204
(b) Sunglasses

FID |  Prec. |  Recall
---|---|---
39.96 |  0.70 |  0.024
37.68 |  0.30 |  0.034
33.29 |  0.39 |  0.089
31.97 |  0.57 |  0.076
(c) Sketches

Table A2:  Ablation study on architecture designs. A two-layer multi-layer perceptron (MLP) is introduced as the heavy version of the attribute adaptor and the attribute classifier, respectively. Models | Babies | Sunglasses | Sketches
---|---|---|---
Heavy attribute adaptor | 290.22 | 262.32 | 114.25
Heavy attribute classifier | 132.14 | 115.88 | 109.23
Lightweight design | 80.16 | 44.96 | 87.55

##  Appendix A Ablation Study

Component Ablation. In order to further investigate the role of each proposed
component (i.e., attribute adaptor, attribute classifier and truncation
strategy), we conduct comprehensive ablation studies on 10-shot adaptation. In
particular, we choose FreezeD (Mo et al., 2020) as our baseline model which
freezes the lower features of the discriminator while fine-tunes the rest of
the discriminator and the entire generator. Moreover, we also apply adaptive
discriminator augmentation strategy (ADA) on it since data augmentations
usually help alleviate the overfitting problem.

As is shown in Tab. A1c, the baseline alternative could achieve the satisfying
synthesis quality but worse diversity, suggesting that it might overfit the
training shot significantly. By involving the attribute adaptor, the baseline
is substantially improved, which already outperforms prior approaches.
Meanwhile, the boosted diversity also demonstrates our motivation that we
could reuse the variation factors of the source model and hence maintain the
synthesis diversity to some extent. However, the quality (i.e., precision) on
babies and sketches becomes worse. After being equipped with the attribute
classifier, the overall measurement is further enhanced. Together with the
diversity-constraint strategy, our GenDA could facilitate the quality to the
baseline level but achieve better diversity, leading to the new state-of-the-
art few-shot adaptation performances.

Architectural Ablation. To further study the effect of the lightweight design
of the attribute adaptor and the attribute classifier, we conduct experiments
of replacing the lightweight module with two-layer multi0layer perceptron
(MLP), which has a higher learning capacity. Tab. A2 presents the results.
Obviously, with either a heavier attribute adaptor or a heavier attribute
classifier, the synthesis performance drops significantly. This might be
caused by the overfitting problem since we only have one training image under
our one-shot setting. It verifies the effectiveness and necessity of our
lightweight design for the challenging one-shot generative domain adaptation
task.

Hyper-parameter Ablation. We also conduct the ablation of hyper-parameter
$\beta$ in Eq. (5). Specifically, we train our model with 10-shot sunglasses
images and maintain the evaluation metrics used in Sec. 3.2. As is shown in
Tab. A3, when increasing the value of $\beta$, the synthesis quality becomes
worse while the diversity is enhanced. Noticeably, when the $\beta$ is less
than 0.5, the training diverges. Therefore, we choose 0.7 in all experiments
for its overall performance.

Table A3:  Ablation study on the strength of diversity-constraint strategy, which is noted as $\beta$ in Eq. (5). Here, a smaller $\beta$ denotes a stronger diversity constraint. Evaluation metrics include FID (lower is better), precision (higher means better quality), and recall (higher means higher diversity).  $\beta$ | FID | Precision | Recall
---|---|---|---
0.5 | 24.87 | 0.82 | 0.163
0.7 | 22.62 | 0.78 | 0.204
0.9 | 24.32 | 0.73 | 0.269
1.0 | 25.29 | 0.71 | 0.349
Figure A1:  Analysis on the transformation vector for domain adaptation, which
is noted as ${\rm\bf a}$ in Eq. (3). We launch 15 one-shot adaptation
experiments with 5 kids, 5 sunglasses, and 5 sketches as the target,
respectively. After the model converges, we collect the transformation vector
learned by the attribute adaptor, and then perform PCA on all 15 vectors. We
can tell that our GenDA tends to learn similar transformation regarding the
reference with similar characters. The 15 training samples are visualized
together with the PCA results.  Figure A2:  Visualization of transformed
latent spaces after domain adaptation. Each cluster corresponds to a
particular training set. Left: The latent spaces learned with different
reference images are clearly separable. Right: When we use two reference
images, which have common attributes, as a combined training set, the
transformed representations tend to locate at the overlapped region between
the representations learned from each reference image independently. Figure
A3:  Latent representation comparison before and after adaptation. The latent
space is clearly pushed from the red cluster to the blue cluster when there
exists an obvious gap between the source and the target domains.

##  Appendix B Analysis on Transformation Vector

We also visualize the learned attributes via Principal components analysis
(PCA). Concretely, we first choose 15 shots from 3 domains (i.e., babies,
sunglasses and sketches). The corresponding models are trained on them and 15
attribute vectors in Eq. (3) are collected to perform PCA. Fig. A1 presents
the PCA results. Obviously, the attribute vectors of different shots but the
same domain assemble closely, while there are obvious decision boundaries
across domains. Such interpretation happens to match our motivation that the
attribute adaptor could capture the representative attributes and make the
corresponding adjustment for a source model to multiple target domains.

##  Appendix C Analysis on Transformed Latent Representation

We also visualize how the latent representations change before and after
adaptation, with the help of PCA. Specifically, we sample $2K$ latent codes
from the latent space after adaptation for each model used in Fig. 4 and Fig.
5. Fig. A2 suggests that the distributions after adapting the source model to
three different target domains are clearly separable. Also, when using more
than one reference image, which have common attributes, from the target
domain, the transformed representations tend to locate at the overlapped
region between the representations learned from each reference image
independently. Fig. A3 shows the latent representation change after cross-
domain adaptation, where the latent distribution of the source domain is
successfully shifted to the target one.

Figure A4:  Comparison with the style transfer task. GenDA could transfer the
characteristic of the target image to the source model in more harmonious way,
while SWAG (Wang et al., 2021) transfers the color inconsistently, leading to
obvious artifacts.

##  Appendix D Additional Results

Comparison with the style transfer task. As discussed in Sec. 3, the proposed
approach might be an alternative for style transfer. Here, we qualitatively
compare our approach with the state-of-the-art method, SWAG (Wang et al.,
2021) regarding the style transfer task. The comparison results are included
in Fig. A4. From the perspective of the color transfer, SWAG (Wang et al.,
2021) does better since it could successfully transfer the representative
color from the target image to the synthesis. For example, the green of the
vegetation and the pink of the skin appears in the transferred samples.
However, it also reveal its weakness that the transfer is inconsistent.
Specifically, the wall or the sky in church synthesis is in pink while our
method pictures them in the same color. This implies that the style transfer
based methods could have no semantic concepts regarding to the content,
leading to such inconsistency and obvious artifacts. Therefore, it naturally
cannot transfer higher-level attributes like sunglasses while ours could.

Figure A5:  Visual relation of one-shot adaptation with different target
samples. Here, for each model (i.e., faces and churches), each column is
generated with the same latent code. Our GenDA is able to transfer the most
distinguishable attributes of the target domain, but almost maintains other
semantics (e.g., the face pose and the church shape).  Figure A6:  Visual
relation of cross-domain adaptation. Here, for each model (i.e., faces and
churches), each column is generated with the same latent code. We can tell
that, under the cross-domain setting, our GenDA can still inherit some
characteristics from the source model (e.g., the face pose and the church
shape).

The visual relation before and after adaptation. A byproduct of our proposed
GenDA is that we manage to inherit some characteristics from the source model
in the process of domain adaptation. Here, we take the images synthesized by
the source generator as the references, and feed the adapted generator with
the same latent codes. In this way, we can check how the synthesis varies
before and after adaptation. Fig. A5 and Fig. A6 correspond to the settings of
Fig. 4 and Fig. 5, respectively. We can tell that many attributes are
maintained in the adapting process, verifying our motivation, which is that
the most of variation factors could be reused in the task of generative domain
adaptation.

##  Appendix E Interpolation of Latent Vectors

Here, we perform the semantic interpolation to evaluate the tuned generator.
Typically, the interpolation at the latent space (Radford et al., 2015) could
interpolate the semantics of two images, which is widely used to measure the
diversity of generative models. Fig. A7 and Fig. A8 present the semantic
interpolation of face and church model respectively. In particular, all models
are fine-tuned with one shot image. Obviously, satisfying interpolation
_i.e._ , smoothing change is clearly observed, even if the model is fine-tuned
with one shot image.

Figure A7:  Semantic interpolation with the model pre-trained on FFHQ human
faces (Karras et al., 2019). The first and last columns are the synthesized
images with two different latent codes after one-shot domain adaption, while
the remaining columns are the outputs by linearly interpolating the two codes.
All intermediate results are still high-quality faces, with gradually varying
semantics (e.g., the gender and the face pose).  Figure A8:  Semantic
interpolation with the model pre-trained on LSUN Churches (Yu et al., 2015).
The first and last columns are the synthesized images with two different
latent codes after one-shot domain adaption, while the remaining columns are
the outputs by linearly interpolating the two codes. All intermediate results
are still high-quality churches, with gradually varying semantics (e.g., the
church shape and the cloud in the sky).