Domain adaptation of GANs is a problem of fine-tuning the state-of-the-art GAN models (e.g. StyleGAN) pretrained on a large dataset to a specific domain with few samples (e.g. painting faces, sketches, etc.). While there are a great number of 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. First, 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. In particular, we show that affine layers of StyleGAN can be sufficient for fine-tuning to similar domains. Second, inspired by these findings, we investigate StyleSpace to utilize it for domain adaptation. We show that there exist directions in the StyleSpace that can adapt StyleGAN to new domains. Further, we examine these directions and discover their many surprising properties. Finally, we leverage our analysis and findings to deliver practical improvements and applications in such standard tasks as image-to-image translation and cross-domain morphing.

具有自适应缩放不同功能的方法在解决鞍点问题方面起着关键作用，这主要是由于亚当在解决对抗机器学习问题（包括gans训练）方面的受欢迎程度。本文对解决SPPS的以下缩放技术进行了理论分析：众所周知的Adam和Rmsprop缩放以及基于Hutchison近似的较新的Adahessian和Oasis。我们将额外的梯度及其改进的版本带有负动量作为基本方法。关于gan的实验研究不仅对亚当，而且对其他不太流行的方法显示出良好的适用性。

生成的对抗网络最近在神经声音中表现出了出色的表现，表现优于最佳自动回归和基于流动的模型。在本文中，我们表明这种成功可以扩展到有条件音频的其他任务。特别是，在HIFI Vocoders的基础上，我们为带宽扩展和语音增强的新型HIFI ++一般框架提出了新颖的一般框架。我们表明，通过改进的生成器体系结构和简化的多歧视培训，HIFI ++在这些任务中的最先进的情况下表现更好或与之相提并论，同时花费大量的计算资源。通过一系列广泛的实验，我们的方法的有效性得到了验证。