4 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.CV2022
Unsupervised Domain Adaptation for Segmentation with Black-box Source Model
Xiaofeng Liu, Chaehwa Yoo, Fangxu Xing +3
Unsupervised domain adaptation (UDA) has been widely used to transfer knowledge from a labeled source domain to an unlabeled target domain to counter the difficulty of labeling in…
cs.CV2022★ 4 cited
Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives
Xiaofeng Liu, Chaehwa Yoo, Fangxu Xing +4
Deep learning has become the method of choice to tackle real-world problems in different domains, partly because of its ability to learn from data and achieve impressive performanc…