Transfer learning of StyleGAN has recently shown great potential to solve diverse tasks, especially in domain translation. Previous methods utilized a source model by swapping or freezing weights during transfer learning, however, they have limitations on visual quality and controlling source features. In other words, they require additional models that are computationally demanding and have restricted control steps that prevent a smooth transition. In this paper, we propose a new approach to overcome these limitations. Instead of swapping or freezing, we introduce a simple feature matching loss to improve generation quality. In addition, to control the degree of source features, we train a target model with the proposed strategy, FixNoise, to preserve the source features only in a disentangled subspace of a target feature space. Owing to the disentangled feature space, our method can smoothly control the degree of the source features in a single model. Extensive experiments demonstrate that the proposed method can generate more consistent and realistic images than previous works.

翻译：StyleGAN的迁移学习近年来在解决多样化任务中展现出巨大潜力，尤其在域翻译领域。以往方法通过权重交换或冻结实现源模型迁移，但存在视觉质量受限和源特征控制能力不足的问题。换言之，这些方法需要额外模型，计算成本高昂，且控制步骤受限，难以实现平滑过渡。本文提出一种新方法以克服上述局限。不同于权重交换或冻结，我们引入简洁的特征匹配损失函数以提升生成质量。此外，为控制源特征的程度，我们采用所提策略FixNoise训练目标模型，使源特征仅保留在目标特征空间的解耦子空间中。得益于解耦特征空间，本方法可在单一模型中平滑控制源特征的程度。大量实验表明，所提方法能生成比以往工作更一致且真实的图像。