Unsupervised image-to-image translation aims to learn the mapping between two visual domains with unpaired samples. Existing works focus on disentangling domain-invariant content code and domain-specific style code individually for multimodal purposes. However, less attention has been paid to interpreting and manipulating the translated image. In this paper, we propose to separate the content code and style code simultaneously in a unified framework. Based on the correlation between the latent features and the high-level domain-invariant tasks, the proposed framework demonstrates superior performance in multimodal translation, interpretability and manipulation of the translated image. Experimental results show that the proposed approach outperforms the existing unsupervised image translation methods in terms of visual quality and diversity.

翻译：未受监督的图像到图像翻译旨在学习两个具有未受保护样本的视觉域之间的绘图; 现有工作重点是为多式联运目的将域变量内容代码和具体域名风格代码单独脱钩; 但是,对翻译图像的解释和操控不够重视; 在本文件中,我们提议在一个统一的框架内同时将内容代码和样式代码分开; 根据潜在特征与高级别域名变量任务之间的相互关系,拟议框架显示在多式翻译、可解释性和对翻译图像的操纵方面表现优异; 实验结果显示,拟议方法在视觉质量和多样性方面超过了现有的不受监督的图像翻译方法。