  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.
