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• W3178061036 abstract "Domain Adaptation has been widely used to deal with the distribution shift in vision, language, multimedia etc. Most domain adaptation methods learn domain-invariant features with data from both domains available. However, such a strategy might be infeasible in practice when source data are unavailable due to data-privacy concerns. To address this issue, we propose a novel adaptation method via hypothesis transfer without accessing source data at adaptation stage. In order to fully use the limited target data, a semi-supervised mutual enhancement method is proposed, in which entropy minimization and augmented label propagation are used iteratively to perform inter-domain and intra-domain alignments. Compared with state-of-the-art methods, the experimental results on three public datasets demonstrate that our method gets up to 19.9% improvements on semi-supervised adaptation tasks." @default.
• W3178061036 created "2021-07-19" @default.
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• W3178061036 date "2021-07-14" @default.
• W3178061036 modified "2023-09-26" @default.
• W3178061036 title "Semi-Supervised Hypothesis Transfer for Source-Free Domain Adaptation." @default.
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• W3178061036 hasPublicationYear "2021" @default.
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