164 citations · 293 across the 20 of their papers we have counts for
11 papers
Causality Inspired Representation Learning for Domain Generalization
Fangrui Lv, Jian Liang, Shuang Li +4
Domain generalization (DG) is essentially an out-of-distribution problem, aiming to generalize the knowledge learned from multiple source domains to an unseen target domain. The ma…
Domain Adaptation via Prompt Learning
Chunjiang Ge, Rui Huang, Mixue Xie +4
Unsupervised domain adaption (UDA) aims to adapt models learned from a well-annotated source domain to a target domain, where only unlabeled samples are given. Current UDA approach…
Learning Temporal Rules from Noisy Timeseries Data
Karan Samel, Zelin Zhao, Binghong Chen +4
Events across a timeline are a common data representation, seen in different temporal modalities. Individual atomic events can occur in a certain temporal ordering to compose highe…
MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition
Shuang Li, Kaixiong Gong, Chi Harold Liu +3
Real-world training data usually exhibits long-tailed distribution, where several majority classes have a significantly larger number of samples than the remaining minority classes…
Dynamic Domain Adaptation for Efficient Inference
Shuang Li, Jinming Zhang, Wenxuan Ma +2
Domain adaptation (DA) enables knowledge transfer from a labeled source domain to an unlabeled target domain by reducing the cross-domain distribution discrepancy. Most prior DA ap…
Transferable Semantic Augmentation for Domain Adaptation
Shuang Li, Mixue Xie, Kaixiong Gong +3
Domain adaptation has been widely explored by transferring the knowledge from a label-rich source domain to a related but unlabeled target domain. Most existing domain adaptation a…