Library

Unsupervised Domain Adaptation Through Transferring both the Source-Knowledge and Target-Relatedness Simultaneously

Unsupervised domain adaptation (UDA) is an emerging research topic in the field of machine learning and pattern recognition, which aims to help the learning of unlabeled target domain by transferring knowledge from the source domain.

cs.LGstat.MLDomain Adaptation and Few-Shot LearningMultimodal Machine Learning ApplicationsSpeech Recognition and Synthesis
Source

Paper

Similar papers

Unsupervised domain adaptation through transferring both the source-knowledge and target-relatedness simultaneouslyQing Tian, Yanan Zhu, Yao Cheng et al.2022 · Electronic Research Archive In Library
Unsupervised Multi-source Domain Adaptation Without Access to Source DataSk Miraj Ahmed, Dripta S. Raychaudhuri, Sujoy Paul et al.2021 In Library
Unsupervised New-set Domain Adaptation with Self-supervised KnowledgeYunyun Wang, Guwei Sun, Guoxiang Zhao et al.2022 · International Journal of Software and Informatics · 1 citation In Library
A Survey on Unsupervised DomainAdaptationPei Wang2025 · International Journal of Artificial Intelligence for Science (IJAI4S) In Library
Unsupervised Multi-source Domain Adaptation Without Access to Source DataSk Miraj Ahmed, Dripta S. Raychaudhuri, Sujoy Paul et al.2021 · arXiv (Cornell University) · 158 citations In Library
Unsupervised Multitarget Domain Adaptation With Dictionary-Bridged Knowledge ExploitationQing Tian, Chuang Ma, Meng Cao et al.2022 · IEEE Transactions on Neural Networks and Learning Systems · 11 citations In Library
Universal Domain AdaptationKaichao You, Mingsheng Long, Zhangjie Cao et al.2019 · Computer Vision and Pattern Recognition · 576 citations In Library
One-step Domain Adaptation Approach with Partial LabelGuohang Zeng, Zhen Fang, Guangquan Zhang et al.2023 In Library
Assisted Unsupervised Domain AdaptationCheng Chen, Jiawei Zhang, Jie Ding et al.2023 In Library
Unsupervised domain adaptation with post-adaptation labeled domain performance preservationHaidi Badr, Nayer Wanas, Magda B. Fayek2022 · Machine Learning with Applications In Library
© 2026 NYSGPT2525 LLC