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Hierarchical Optimal Transport for Unsupervised Domain Adaptation
[article]
2021
arXiv
pre-print
In this paper, we propose a novel approach for unsupervised domain adaptation, that relates notions of optimal transport, learning probability measures and unsupervised learning. The proposed approach, HOT-DA, is based on a hierarchical formulation of optimal transport, that leverages beyond the geometrical information captured by the ground metric, richer structural information in the source and target domains. The additional information in the labeled source domain is formed instinctively by
arXiv:2112.02073v1
fatcat:yn7mek3xwzhstka6bubcvpwmu4