Unpaired image-to-image translation (UNIT) aims to map images between two
visual domains without paired training data. However, given a UNIT model
trained on certain domains, it is difficult for current methods to incorporate
new domains because they often need to train the full model on both existing
and new domains. To address this problem, we propose a new domain-scalable UNIT
method, termed as latent space anchoring, which can be efficiently extended to
new visual domains and does not need to fine-tune encoders and decoders of
existing domains. Our method anchors images of different domains to the same
latent space of frozen GANs by learning lightweight encoder and regressor
models to reconstruct single-domain images. In the inference phase, the learned
encoders and decoders of different domains can be arbitrarily combined to
translate images between any two domains without fine-tuning. Experiments on
various datasets show that the proposed method achieves superior performance on
both standard and domain-scalable UNIT tasks in comparison with the
state-of-the-art methods.

提出了一种新颖的图像生成方法：latent space anchoring，可以实现在不需要调整现有域的编解码器的情况下扩展到新的视觉域，该方法通过学习轻量级编码器和回归器来将不同域的图像锚定到相同的冻结 GANs 的潜在空间中，并且其编码器和解码器可以任意组合进行图像翻译，实验表明，该方法在标准和可伸缩的 UNIT 任务上均表现优异。