Unsupervised Image-to-Image Translation Networks
Unsupervised Image-to-Image Translation Networks
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发表时间：
2017-03
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通讯作者：
Ming-Yu Liu;T. Breuel;J. Kautz
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作者：
Ming-Yu Liu;T. Breuel;J. Kautz
无监督图像到图像翻译的目的是通过使用来自各个域中的边缘分布的图像来学习不同域中的图像的联合分布。由于存在一个无限多的联合分布集，可以到达给定的边缘分布，人们不能推断出任何关于联合分布的边缘分布没有额外的假设。为了解决这个问题，我们提出了一个共享潜在空间假设，并提出了一个基于耦合GAN的无监督图像到图像翻译框架。我们将所提出的框架与竞争方法进行比较，并在各种具有挑战性的无监督图像翻译任务上呈现高质量的图像翻译结果，包括街景图像翻译，动物图像翻译和人脸图像翻译。我们还将所提出的框架应用于域自适应，并在基准数据集上实现了最先进的性能。代码和其他结果可在此https URL中找到。
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 .