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. [] Key Method Because this mapping is highly under-constrained, we couple it with an inverse mapping F : Y → X and introduce a cycle consistency loss to push F(G(X)) ≈ X (and vice versa). Qualitative results are presented on several tasks where paired training data does not exist, including collection style…

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