Recently, several methods based on generative adversarial network (GAN) have been proposed for the task of aligning cross-domain images or learning a joint distribution of cross-domain images. One of the methods is to use conditional GAN for alignment. However, previous attempts of adopting conditional GAN do not perform as well as other methods. In this work we present an approach for improving the capability of the methods which are based on conditional GAN. We evaluate the proposed method on numerous tasks and the experimental results show that it is able to align the cross-domain images successfully in absence of paired samples. Furthermore, we also propose another model which conditions on multiple information such as domain information and label information. Conditioning on domain information and label information, we are able to conduct label propagation from the source domain to the target domain. A 2-step alternating training algorithm is proposed to learn this model.

本论文提出了一种基于条件GAN的方法，可以在缺少配对样本的情况下成功地对齐跨域图像，并且还提出了一种利用多个信息条件（如域信息和标签信息）进行标签传播的模型，并给出了一个用于学习该模型的两步交替训练算法。

AlignGAN：使用有条件的生成对抗网络学习对齐跨域图像