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Towards Instance-level Image-to-Image Translation
[article]
2019
arXiv
pre-print
Unpaired Image-to-image Translation is a new rising and challenging vision problem that aims to learn a mapping between unaligned image pairs in diverse domains. Recent advances in this field like MUNIT and DRIT mainly focus on disentangling content and style/attribute from a given image first, then directly adopting the global style to guide the model to synthesize new domain images. However, this kind of approaches severely incurs contradiction if the target domain images are content-rich
arXiv:1905.01744v1
fatcat:vicgo5i7vvhddpb36tvgavfelu