16 citations · 41 across the 9 of their papers we have counts for
14 papers · 1 filter
Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation
Xiaofeng Liu, Site Li, Yubin Ge +3
The unsupervised domain adaptation (UDA) has been widely adopted to alleviate the data scalability issue, while the existing works usually focus on classifying independently discre…
Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate
Xiaofeng Liu, Zhenhua Guo, Site Li +5
In this work, we propose an adversarial unsupervised domain adaptation (UDA) approach with the inherent conditional and label shifts, in which we aim to align the distributions w.r…
Embedding Semantic Hierarchy in Discrete Optimal Transport for Risk Minimization
Yubin Ge, Site Li, Xuyang Li +4
The widely-used cross-entropy (CE) loss-based deep networks achieved significant progress w.r.t. the classification accuracy. However, the CE loss can essentially ignore the risk o…
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…
Identity-aware Facial Expression Recognition in Compressed Video
Xiaofeng Liu, Linghao Jin, Xu Han +3
This paper targets to explore the inter-subject variations eliminated facial expression representation in the compressed video domain. Most of the previous methods process the RGB…
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…