Jul 2, 2015 · In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source ...
Oct 7, 2016 · In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source ...
In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target ...
Mar 18, 2024 · The optimal transport solution gives us a matching between source and target domain mixture components. From this matching, we can map data ...
Universal Domain Adaptation (UniDA) aims to transfer knowledge from a source domain to a target domain without any constraints on label sets.
In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target ...
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OT for domain adaptation . This example introduces a domain adaptation in a 2D setting and the 4 OTDA approaches currently supported in POT.
Heterogeneous domain adaptation (HDA) aims to exploit knowledge from a heterogeneous source do- main to improve the learning performance in a tar- get domain.
Optimal transport has been applied in domain adaptation to align the representations in the source and target domains with associated theoretical guarantees.
By using the proposed optimal transport with label regularization, we obtain significant increase in performance compared to the original transport solution.