Generative domain adaptation has achieved remarkable progress, enabling us to
adapt a pre-trained generator to a new target domain. However, existing methods
simply adapt the generator to a single target domain and are limited to a
single modality, either text-driven or image-driven. Moreover, they are prone
to overfitting domain-specific attributes, which inevitably compromises
cross-domain consistency. In this paper, we propose UniHDA, a unified and
versatile framework for generative hybrid domain adaptation with multi-modal
references from multiple domains. We use CLIP encoder to project multi-modal
references into a unified embedding space and then linear interpolate the
direction vectors from multiple target domains to achieve hybrid domain
adaptation. To ensure the cross-domain consistency, we propose a novel
cross-domain spatial structure (CSS) loss that maintains detailed spatial
structure information between source and target generator. Experiments show
that the adapted generator can synthesise realistic images with various
attribute compositions. Additionally, our framework is versatile to multiple
generators, \eg, StyleGAN2 and Diffusion Models.

提出了 UniHDA，一个统一且多用途的混合领域适应框架，利用来自多个领域的多模态参考将生成器适应到新的目标领域，并提出了一种新颖的跨领域空间结构（CSS）损失来保持源生成器与目标生成器之间的详细空间结构信息，实验证明该适应后的生成器能够综合具有不同属性组成的逼真图像。

UniHDA：走向通用的混合领域自适应图像生成模型

UniHDA: Towards Universal Hybrid Domain Adaptation of Image Generators

Face anti-spoofing (FAS) approaches based on unsupervised domain adaption
(UDA) have drawn growing attention due to promising performances for target
scenarios. Most existing UDA FAS methods typically fit the trained models to
the target domain via aligning the distribution of semantic high-level
features. However, insufficient supervision of unlabeled target domains and
neglect of low-level feature alignment degrade the performances of existing
methods. To address these issues, we propose a novel perspective of UDA FAS
that directly fits the target data to the models, i.e., stylizes the target
data to the source-domain style via image translation, and further feeds the
stylized data into the well-trained source model for classification. The
proposed Generative Domain Adaptation (GDA) framework combines two carefully
designed consistency constraints: 1) Inter-domain neural statistic consistency
guides the generator in narrowing the inter-domain gap. 2) Dual-level semantic
consistency ensures the semantic quality of stylized images. Besides, we
propose intra-domain spectrum mixup to further expand target data distributions
to ensure generalization and reduce the intra-domain gap. Extensive experiments
and visualizations demonstrate the effectiveness of our method against the
state-of-the-art methods.

本文提出了一种基于生成领域适应的人脸防欺骗方法，利用图像翻译的方式将目标数据转换到源域风格，再通过双重的一致性约束保证良好的分类性能和语义一致性。实验证明该方法在人脸防欺骗方面的效果明显高于现有的先进算法。