Domain adaptation framework of GANs has achieved great progress in recent years as a main successful approach of training contemporary GANs in the case of very limited training data. In this work, we significantly improve this framework by proposing an extremely compact parameter space for fine-tuning the generator. We introduce a novel domain-modulation technique that allows to optimize only 6 thousand-dimensional vector instead of 30 million weights of StyleGAN2 to adapt to a target domain. We apply this parameterization to the state-of-art domain adaptation methods and show that it has almost the same expressiveness as the full parameter space. Additionally, we propose a new regularization loss that considerably enhances the diversity of the fine-tuned generator. Inspired by the reduction in the size of the optimizing parameter space we consider the problem of multi-domain adaptation of GANs, i.e. setting when the same model can adapt to several domains depending on the input query. We propose the HyperDomainNet that is a hypernetwork that predicts our parameterization given the target domain. We empirically confirm that it can successfully learn a number of domains at once and may even generalize to unseen domains. Source code can be found at https://github.com/MACderRu/HyperDomainNet

翻译：生成对抗网络的域自适应框架近年来取得了显著进展，成为在训练数据极其有限的情况下训练当代GANs的主流成功方法。本文通过提出一种用于微调生成器的极端紧凑参数空间，显著改进了该框架。我们引入了一种新颖的域调制技术，该技术仅需优化6000维向量而非StyleGAN2的3000万权重即可适应目标域。我们将这种参数化方法应用于最先进的域自适应方法，并证明其具有几乎与完整参数空间相同的表达能力。此外，我们提出了一种新的正则化损失函数，显著增强了微调生成器的多样性。受优化参数空间规模缩减的启发，我们考虑了GANs的多域自适应问题，即同一模型可根据输入查询适应多个域的场景。我们提出了HyperDomainNet——一种可根据目标域预测参数化的超网络。实验证实，该网络能够同时成功学习多个域，甚至可泛化至未见过的域。源代码可在https://github.com/MACderRu/HyperDomainNet获取。