Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping

Unsupervised domain mapping aims to learn a function to translate domain X to Y by a function GXY in the absence of paired examples. Finding the optimal GXY without paired data is an ill-posed problem, so appropriate constraints are required to obtain reasonable solutions... (read more)

PDF Abstract CVPR 2019 PDF CVPR 2019 Abstract
No code implementations yet. Submit your code now

Tasks


Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper