Image-to-image translation has drawn great attention during the past few
years. It aims to translate an image in one domain to a given reference image
in another domain. Due to its effectiveness and efficiency, many applications
can be formulated as image-to-image translation problems. However, three main
challenges remain in image-to-image translation: 1) the lack of large amounts
of aligned training pairs for different tasks; 2) the ambiguity of multiple
possible outputs from a single input image; and 3) the lack of simultaneous
training of multiple datasets from different domains within a single network.
We also found in experiments that the implicit disentanglement of content and
style could lead to unexpect results. In this paper, we propose a unified
framework for learning to generate diverse outputs using unpaired training data
and allow simultaneous training of multiple datasets from different domains via
a single network. Furthermore, we also investigate how to better extract domain
supervision information so as to learn better disentangled representations and
achieve better image translation. Experiments show that the proposed method
outperforms or is comparable with the state-of-the-art methods.

本文提出了一个使用无成对训练数据和单个网络同时训练不同域的多个数据集来学习生成多样化输出的统一框架，并研究了如何更好地提取域监督信息以学习更好的分离表征和实现更好的图像翻译的方法。实验表明，所提出的方法优于或与最先进的方法相当。