Class conditional distribution alignment for domain adaptation
Kai CAO1; Zhipeng TU1; Yang MING1
Source Publication控制理论与技术:英文版
AbstractIn this paper,we study the problem of domain adaptation,which is a crucial ingredient in transfer learning with two domains,that is,the source domain with labeled data and the target domain with none or few labels.Domain adaptation aims to extract knowledge from the source domain to improve the performance of the learning task in the target domain.A popular approach to handle this problem is via adversarial training,which is explained by the H△H-distance theory.However,traditional adversarial network architectures just align the marginal feature distribution in the feature space.The alignment of class condition distribution is not guaranteed.Therefore,we proposed a novel method based on pseudo labels and the cluster assumption to avoid the incorrect class alignment in the feature space.The experiments demonstrate that our framework improves the accuracy on typical transfer learning tasks.
KeywordDomain adaptation distribution alignment feature cluster
Indexed ByCSCD
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Document Type期刊论文
Affiliation1.Key Lab of Systems and Control,Academy of Mathematics and Systems Science,Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Kai CAO,Zhipeng TU,Yang MING. Class conditional distribution alignment for domain adaptation[J]. 控制理论与技术:英文版,2020,18.0(1.0):72-80.
APA Kai CAO,Zhipeng TU,&Yang MING.(2020).Class conditional distribution alignment for domain adaptation.控制理论与技术:英文版,18.0(1.0),72-80.
MLA Kai CAO,et al."Class conditional distribution alignment for domain adaptation".控制理论与技术:英文版
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