Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks

@article{Zhu2017UnpairedIT,
  title={Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks},
  author={Jun-Yan Zhu and Taesung Park and Phillip Isola and Alexei A. Efros},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={2242-2251}
}
Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, for many tasks, paired training data will not be available. We present an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples. Our goal is to learn a mapping G : X → Y such that the distribution of images from G(X) is… CONTINUE READING
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