8 citations · 12 across the 4 of their papers we have counts for
4 papers
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…
Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference
Xiaofeng Liu, Bo Hu, Linghao Jin +6
In this work, we propose a domain generalization (DG) approach to learn on several labeled source domains and transfer knowledge to a target domain that is inaccessible in training…
Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis
Xiaofeng Liu, Xiongchang Liu, Bo Hu +7
Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the…
Energy-constrained Self-training for Unsupervised Domain Adaptation
Xiaofeng Liu, Bo Hu, Xiongchang Liu +3
Unsupervised domain adaptation (UDA) aims to transfer the knowledge on a labeled source domain distribution to perform well on an unlabeled target domain. Recently, the deep self-t…