We present a new method for one shot domain adaptation. The input to our method is trained GAN that can produce images in domain A and a single reference image I_B from domain B. The proposed algorithm can translate any output of the trained GAN from domain A to domain B. There are two main advantages of our method compared to the current state of the art: First, our solution achieves higher visual quality, e.g. by noticeably reducing overfitting. Second, our solution allows for more degrees of freedom to control the domain gap, i.e. what aspects of image I_B are used to define the domain B. Technically, we realize the new method by building on a pre-trained StyleGAN generator as GAN and a pre-trained CLIP model for representing the domain gap. We propose several new regularizers for controlling the domain gap to optimize the weights of the pre-trained StyleGAN generator to output images in domain B instead of domain A. The regularizers prevent the optimization from taking on too many attributes of the single reference image. Our results show significant visual improvements over the state of the art as well as multiple applications that highlight improved control.

介绍了一种用于一次性域自适应的新方法，使用训练有素的 GAN 可以在域 A 和域 B 中生成图像，通过提出的算法可以将训练有素的 GAN 的任何输出从域 A 转换到域 B，相对于目前的技术水平，该解决方案有两个主要优点：首先，我们的解决方案实现了更高的视觉质量，例如显着减少了过度拟合；其次，我们的解决方案允许更多的自由度来控制域差距，即使用图像 I_B 的哪些方面来定义域 B。

单镜头域自适应的域间差异控制：一个生成对抗网络方法