34 citations · 213 across the 28 of their papers we have counts for
5 papers · 1 filter
Constraining Pseudo-label in Self-training Unsupervised Domain Adaptation with Energy-based Model
Lingsheng Kong, Bo Hu, Xiongchang Liu +3
Deep learning is usually data starved, and the unsupervised domain adaptation (UDA) is developed to introduce the knowledge in the labeled source domain to the unlabeled target dom…
Subtype-Aware Dynamic Unsupervised Domain Adaptation
Xiaofeng Liu, Fangxu Xing, Jia You +4
Unsupervised domain adaptation (UDA) has been successfully applied to transfer knowledge from a labeled source domain to target domains without their labels. Recently introduced tr…
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…
ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-training
Xiaofeng Liu, Fangxu Xing, Nadya Shusharina +4
Unsupervised domain adaptation (UDA) has been vastly explored to alleviate domain shifts between source and target domains, by applying a well-performed model in an unlabeled targe…
Self-semantic contour adaptation for cross modality brain tumor segmentation
Xiaofeng Liu, Fangxu Xing, Georges El Fakhri +1
Unsupervised domain adaptation (UDA) between two significantly disparate domains to learn high-level semantic alignment is a crucial yet challenging task.~To this end, in this work…