37 citations · 46 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 9 cited
Generation, augmentation, and alignment: A pseudo-source domain based method for source-free domain adaptation
Yuntao Du, Haiyang Yang, Mingcai Chen +3
Conventional unsupervised domain adaptation (UDA) methods need to access both labeled source samples and unlabeled target samples simultaneously to train the model. While in some s…
cs.LG2021★ 37 cited
AdaRNN: Adaptive Learning and Forecasting of Time Series
Yuntao Du, Jindong Wang, Wenjie Feng +4
Time series has wide applications in the real world and is known to be difficult to forecast. Since its statistical properties change over time, its distribution also changes tempo…
cs.LG2021
Cross-domain error minimization for unsupervised domain adaptation
Yuntao Du, Yinghao Chen, Fengli Cui +2
Unsupervised domain adaptation aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Previous methods focus on learning domain-invariant features t…