Unsupervised image-to-image translation aims at learning a joint distribution
of images in different domains by using images from the marginal distributions
in individual domains. Since there exists an infinite set of joint
distributions that can arrive the given marginal distributions, one could infer
nothing about the joint distribution from the marginal distributions without
additional assumptions. To address the problem, we make a shared-latent space
assumption and propose an unsupervised image-to-image translation framework
based on Coupled GANs. We compare the proposed framework with competing
approaches and present high quality image translation results on various
challenging unsupervised image translation tasks, including street scene image
translation, animal image translation, and face image translation. We also
apply the proposed framework to domain adaptation and achieve state-of-the-art
performance on benchmark datasets. Code and additional results are available in
this https URL .

本文介绍了一种基于共享潜在空间假设和 Coupled GANs 的无监督的图像到图像翻译框架，并在多个挑战性的无监督图像翻译任务中取得了高质量的翻译结果，同时也应用于域自适应并实现了最先进的基准数据集性能。