Image-to-image translation has drawn great attention during the past few years. It aims to translate an image in one domain to a target image in another domain. However, three big challenges remain in image-to-image translation: (1) the lack of large amounts of aligned training pairs for various tasks; (2) the ambiguity of multiple possible outputs from a single input image; and (3) the lack of simultaneous training for multi-domain translation with a single network. Therefore in this paper, we propose a unified framework for learning to generate diverse outputs using unpaired training data and allow for simultaneous multi-domain translation via a single model. Moreover, we also observed from experiments that the implicit disentanglement of content and style could lead to undesirable results. Thus we investigate how to extract domain-level signal as explicit supervision so as to achieve better image-to-image translation. Extensive experiments show that the proposed method outperforms or is comparable with the state-of-the-art methods for various applications.

图像到图像的翻译在过去几年中引起了极大的关注。它旨在将一个域中的图像转换为另一域中的目标图像。但是，图像到图像的翻译仍然面临三大挑战：（1）缺乏针对各种任务的大量对齐培训对；（2）来自单个输入图像的多个可能输出的歧义；（3）缺乏同时使用单个网络进行多域翻译的培训。因此，在本文中，我们提出了一个统一的框架，用于学习使用不成对的训练数据生成不同的输出，并允许通过单个模型同时进行多域翻译。此外，我们还从实验中观察到内容和样式的隐式分离可能导致不良结果。因此，我们研究了如何提取域级信号作为显式监督，以实现更好的图像到图像翻译。大量实验表明，所提出的方法优于或可与各种应用的最新方法相媲美。