Can a pre-trained generator be adapted to the hybrid of multiple target
domains and generate images with integrated attributes of them? In this work,
we introduce a new task -- Few-shot Hybrid Domain Adaptation (HDA). Given a
source generator and several target domains, HDA aims to acquire an adapted
generator that preserves the integrated attributes of all target domains,
without overriding the source domain's characteristics. Compared with Domain
Adaptation (DA), HDA offers greater flexibility and versatility to adapt
generators to more composite and expansive domains. Simultaneously, HDA also
presents more challenges than DA as we have access only to images from
individual target domains and lack authentic images from the hybrid domain. To
address this issue, we introduce a discriminator-free framework that directly
encodes different domains' images into well-separable subspaces. To achieve
HDA, we propose a novel directional subspace loss comprised of a distance loss
and a direction loss. Concretely, the distance loss blends the attributes of
all target domains by reducing the distances from generated images to all
target subspaces. The direction loss preserves the characteristics from the
source domain by guiding the adaptation along the perpendicular to subspaces.
Experiments show that our method can obtain numerous domain-specific attributes
in a single adapted generator, which surpasses the baseline methods in semantic
similarity, image fidelity, and cross-domain consistency.

通过引入新的任务 —— 少样本混合领域适应（HDA），本研究在给定源生成器和多个目标领域的情况下，提出了一种方向子空间损失的判别器无关框架，以获取保留所有目标领域综合属性的适应生成器，并保留源领域的特征。实验证明，该方法可以在单个适应生成器中获得许多领域特定属性，超过了基准方法在语义相似度、图像保真度和跨领域一致性方面的表现。

图像生成器的少样本混合域适应

Few-shot Hybrid Domain Adaptation of Image Generators

Thanks to the recent development of deep generative models, it is becoming
possible to generate high-quality images with both fidelity and diversity.
However, the training of such generative models requires a large dataset. To
reduce the amount of data required, we propose a new method for transferring
prior knowledge of the pre-trained generator, which is trained with a large
dataset, to a small dataset in a different domain. Using such prior knowledge,
the model can generate images leveraging some common sense that cannot be
acquired from a small dataset. In this work, we propose a novel method focusing
on the parameters for batch statistics, scale and shift, of the hidden layers
in the generator. By training only these parameters in a supervised manner, we
achieved stable training of the generator, and our method can generate higher
quality images compared to previous methods without collapsing, even when the
dataset is small (~100). Our results show that the diversity of the filters
acquired in the pre-trained generator is important for the performance on the
target domain. Our method makes it possible to add a new class or domain to a
pre-trained generator without disturbing the performance on the original
domain.

利用预训练生成器的先前知识，通过对生成器中的批次统计参数进行有监督训练，实现了在小数据集下生成高质量图像的新方法。