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Semi-Supervised Optimal Transport for Heterogeneous Domain Adaptation
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Heterogeneous domain adaptation (HDA) aims to exploit knowledge from a heterogeneous source domain to improve the learning performance in a target domain. Since the feature spaces of the source and target domains are different, the transferring of knowledge is extremely difficult. In this paper, we propose a novel semi-supervised algorithm for HDA by exploiting the theory of optimal transport (OT), a powerful tool originally designed for aligning two different distributions. To match the
doi:10.24963/ijcai.2018/412
dblp:conf/ijcai/Yan0WMTW18
fatcat:4iejfpaqmrbbdduidxzmcqpj34