XOGAN: One-to-Many Unsupervised Image-to-Image Translation

@article{Zhang2018XOGANOU,
  title={XOGAN: One-to-Many Unsupervised Image-to-Image Translation},
  author={Yongqi Zhang},
  journal={CoRR},
  year={2018},
  volume={abs/1805.07277}
}
Unsupervised image-to-image translation aims at learning the relationship between samples from two image domains without supervised pair information. The relationship between two domain images can be one-to-one, one-to-many or many-to-many. In this paper, we study the one-to-many unsupervised image translation problem in which an input sample from one domain can correspond to multiple samples in the other domain. To learn the complex relationship between the two domains, we introduce an… CONTINUE READING

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