Domain consensual contrastive learning for few-shot universal domain adaptation
Domain consensual contrastive learning for few-shot universal domain adaptation
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New York: Springer US
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English
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New York: Springer US
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Traditional unsupervised domain adaptation (UDA) aims to transfer the learned knowledge from a fully labeled source domain to another unlabeled target domain on the same label set. The strong assumptions of full annotations on the source domain and a closed label set of the two domains might not hold in real-world applications. In this paper, we in...
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Domain consensual contrastive learning for few-shot universal domain adaptation
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TN_cdi_proquest_journals_2881542265
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2881542265
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ISSN
0924-669X
E-ISSN
1573-7497
DOI
10.1007/s10489-023-04890-0
