# Joint Wasserstein Distribution Matching

@article{Cao2020JointWD, title={Joint Wasserstein Distribution Matching}, author={Jiezhang Cao and Langyuan Mo and Qing Du and Yong Guo and Peilin Zhao and Junzhou Huang and Mingkui Tan}, journal={ArXiv}, year={2020}, volume={abs/2003.00389} }

Joint distribution matching (JDM) problem, which aims to learn bidirectional mappings to match joint distributions of two domains, occurs in many machine learning and computer vision applications. This problem, however, is very difficult due to two critical challenges: (i) it is often difficult to exploit sufficient information from the joint distribution to conduct the matching; (ii) this problem is hard to formulate and optimize. In this paper, relying on optimal transport theory, we propose…

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