In this study, we investigate the task of few-shot Generative Domain Adaptation (GDA), which involves transferring a pre-trained generator from one domain to a new domain using one or a few reference images. Building upon previous research that has focused on Target-domain Consistency, Large Diversity, and Cross-domain Consistency, we conclude two additional desired properties for GDA: Memory and Domain Association. To meet these properties, we proposed a novel method Domain Re-Modulation (DoRM). Specifically, DoRM freezes the source generator and employs additional mapping and affine modules (M&A module) to capture the attributes of the target domain, resulting in a linearly combinable domain shift in style space. This allows for high-fidelity multi-domain and hybrid-domain generation by integrating multiple M&A modules in a single generator. DoRM is lightweight and easy to implement. Extensive experiments demonstrated the superior performance of DoRM on both one-shot and 10-shot GDA, both quantitatively and qualitatively. Additionally, for the first time, multi-domain and hybrid-domain generation can be achieved with a minimal storage cost by using a single model. The code will be available at https://github.com/wuyi2020/DoRM.

翻译：在本研究中，我们探讨了少样本生成式域适应（GDA）任务，该任务旨在利用一张或少量参考图像，将预训练生成器从一个域迁移至新域。在先前关注目标域一致性、大多样性及跨域一致性的研究基础上，我们归纳出GDA所需的另外两个理想属性：记忆与域关联性。为满足这些属性，我们提出了一种新颖方法——域重调制（DoRM）。具体而言，DoRM冻结源生成器，并通过附加映射与仿射模块（M&A模块）捕获目标域属性，从而在风格空间中实现线性可组合的域偏移。这允许在单一生成器中集成多个M&A模块，实现高保真的多域及混合域生成。DoRM轻量且易于实现。大量实验在定量与定性层面均证明了DoRM在单样本及10样本GDA任务中的优越性能。此外，首次可通过单一模型以极低存储成本实现多域及混合域生成。代码将开源至https://github.com/wuyi2020/DoRM。