Domain adaptation of GANs is a problem of fine-tuning GAN models pretrained on a large dataset (e.g. StyleGAN) to a specific domain with few samples (e.g. painting faces, sketches, etc.). While there are many methods that tackle this problem in different ways, there are still many important questions that remain unanswered. In this paper, we provide a systematic and in-depth analysis of the domain adaptation problem of GANs, focusing on the StyleGAN model. We perform a detailed exploration of the most important parts of StyleGAN that are responsible for adapting the generator to a new domain depending on the similarity between the source and target domains. As a result of this study, we propose new efficient and lightweight parameterizations of StyleGAN for domain adaptation. Particularly, we show that there exist directions in StyleSpace (StyleDomain directions) that are sufficient for adapting to similar domains. For dissimilar domains, we propose Affine+ and AffineLight+ parameterizations that allows us to outperform existing baselines in few-shot adaptation while having significantly less training parameters. Finally, we examine StyleDomain directions and discover their many surprising properties that we apply for domain mixing and cross-domain image morphing. Source code can be found at https://github.com/AIRI-Institute/StyleDomain.

翻译：摘要：生成对抗网络（GAN）的领域适应问题，旨在将在大规模数据集（如StyleGAN）上预训练的GAN模型微调至具有少量样本的特定领域（例如人脸绘画、素描等）。尽管已有多种方法从不同角度解决该问题，但仍有诸多重要问题悬而未决。本文针对GAN的领域适应问题开展系统性深入分析，聚焦于StyleGAN模型。我们详细探究了StyleGAN中根据源域与目标域相似性实现生成器向新域适应的关键部件。基于此项研究，我们提出了面向领域适应的StyleGAN高效轻量化参数化方案。特别地，我们证明Style空间中存在足以适应相似领域的方向（即StyleDomain方向）。针对非相似领域，我们提出Affine+与AffineLight+参数化方案，在保持训练参数显著减少的同时，在少样本适应任务上超越现有基线模型。最后，我们深入考察StyleDomain方向，发现诸多具有应用价值的特性，并将其应用于领域混合与跨域图像变形。源代码见https://github.com/AIRI-Institute/StyleDomain。