We propose the coupled generative adversarial network (CoGAN) framework for generating pairs of corresponding images in two different domains. It consists of a pair of generative adversarial networks, each responsible for generating images in one domain. We show that by enforcing a simple weight-sharing constraint, the CoGAN learns to generate pairs of corresponding images without existence of any pairs of corresponding images in the two domains in the training set. In other words, the CoGAN learns a joint distribution of images in the two domains from images drawn separately from the marginal distributions of the individual domains. This is in contrast to the existing multi-modal generative models, which require corresponding images for training. We apply the CoGAN to several pair image generation tasks. For each task, the GoGAN learns to generate convincing pairs of corresponding images. We further demonstrate the applications of the CoGAN framework for the domain adaptation and cross-domain image generation tasks.

本文提出了联合生成对抗网络(CoGAN)用于学习多域图像的联合分布。 与现有方法相比，CoGAN可以在不需要不同域中对应图像元组的情况下，仅通过来自边际分布的样本学习联合分布，并将其应用于许多联合分布学习任务，包括颜色和深度图像的联合分布以及具有不同属性的脸部图像的联合分布。 此外，对于域自适应和图像转换，也展示了其应用。

耦合生成对抗网络