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Source-Free Unsupervised Domain Adaptation with sample transport Learning

Journal of Computer Science & Technology 2021年第3期36卷 606-616页

作者： Qing Tian Chuang Ma Feng-Yuan Zhang Shun Peng Hui Xue School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing 210044China School of Computer Science and Engineering Southeast UniversityNanjing 211189China

Unsupervised domain adaptation(UDA)has achieved great success in handling cross-domain machine learning *** typically benefits the model training of unlabeled target domain by leveraging knowledge from labeled source *** this purpose,the minimization of the marginal distribution divergence and conditional distribution divergence between the source and the target domain is widely adopted in existing ***,for the sake of privacy preservation,the source domain is usually not provided with training data but trained predictor(e.g.,classifier).This incurs the above studies infeasible because the marginal and conditional distributions of the source domain are *** this end,this article proposes a source-free UDA which jointly models domain adaptation and sample transport learning,namely sample transport Domain Adaptation(STDA).Specifically,STDA constructs the pseudo source domain according to the aggregated decision boundaries of multiple source classifiers made on the target ***,it refines the pseudo source domain by augmenting it through transporting those target samples with high confidence,and consequently generates labels for the target *** train the STDA model by performing domain adaptation with sample transport between the above steps in alternating manner,and eventually achieve knowledge adaptation to the target domain and attain confident labels for ***,evaluation results have validated effectiveness and superiority of the proposed method.

关键词： unsupervised domain adaptation domain shift sample transport pseudo source domain

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