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• W2897536120 abstract "Unsupervised domain adaptation has caught appealing attentions as it facilitates the unlabeled target learning by borrowing existing well-established source domain knowledge. Recent practice on domain adaptation manages to extract effective features by incorporating the pseudo labels for the target domain to better solve cross-domain distribution divergences. However, existing approaches separate target label optimization and domain-invariant feature learning as different steps. To address that issue, we develop a novel Graph Adaptive Knowledge Transfer (GAKT) model to jointly optimize target labels and domain-free features in a unified framework. Specifically, semi-supervised knowledge adaptation and label propagation on target data are coupled to benefit each other, and hence the marginal and conditional disparities across different domains will be better alleviated. Experimental evaluation on two cross-domain visual datasets demonstrates the effectiveness of our designed approach on facilitating the unlabeled target task learning, compared to the state-of-the-art domain adaptation approaches." @default.
• W2897536120 created "2018-10-26" @default.
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• W2897536120 date "2018-01-01" @default.
• W2897536120 modified "2023-10-15" @default.
• W2897536120 title "Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation" @default.
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• W2897536120 doi "https://doi.org/10.1007/978-3-030-01216-8_3" @default.
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