Concepedia

Concept

domain adaptation

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5.6K

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431.6K

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15.1K

Authors

2.2K

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About

Domain adaptation is a subfield of machine learning focused on techniques that enable models trained on data from a source domain to perform effectively on data from a related but statistically distinct target domain, thereby addressing the challenge of distribution shift. This research area investigates methodologies for transferring knowledge or aligning feature spaces between domains to improve model generalization and performance in scenarios where target domain data is scarce or unlabeled, mitigating the need for extensive target-specific annotation.

Top Authors

Rankings shown are based on concept H-Index.

ML

Tsinghua University

QY

Hong Kong University of Science and Technology

JW

Tsinghua University

KS

University of California, Berkeley

ZD

Northeastern University

Top Institutions

Rankings shown are based on concept H-Index.

Tsinghua University

Beijing, China

University of California, Berkeley

Berkeley, United States