Contrastive Adaptation Network for Single- and Multi-Source Domain Adaptation

Unsupervised domain adaptation (UDA) makes predictions for the target domain data while manual annotations are only available in the source domain. Previous methods minimize the domain discrepancy neglecting the class information, which may lead to misalignment and poor generalization performance. T...

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Bibliographic Details
Published in:	IEEE transactions on pattern analysis and machine intelligence 2022-04, Vol.44 (4), p.1793-1804
Main Authors:	Kang, Guoliang, Jiang, Lu, Wei, Yunchao, Yang, Yi, Hauptmann, Alexander
Format:	Article
Language:	English
Subjects:	Ablation Adaptation Adaptation models Algorithms Annotations Benchmark testing Cluster Analysis Clustering Contrastive Design optimization domain adaptation Domains Estimation Manuals Measurement Misalignment multi-source Neural networks Sampling State-of-the-art reviews Training unsupervised
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