2021
DOI: 10.48550/arxiv.2112.04137
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Pareto Domain Adaptation

Abstract: Domain adaptation (DA) attempts to transfer the knowledge from a labeled source domain to an unlabeled target domain that follows different distribution from the source. To achieve this, DA methods include a source classification objective L S to extract the source knowledge and a domain alignment objective L D to diminish the domain shift, ensuring knowledge transfer. Typically, former DA methods adopt some weight hyper-parameters to linearly combine the training objectives to form an overall objective L. How… Show more

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