36 citations · 103 across the 10 of their papers we have counts for
Showing 2020Show all
3 papers · 1 filter
cs.LG2020★ 11 cited
Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation
Bo Li, Yezhen Wang, Shanghang Zhang +4
The success of supervised learning hinges on the assumption that the training and test data come from the same underlying distribution, which is often not valid in practice due to…
cs.CV2020★ 8 cited
ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation
Sicheng Zhao, Yezhen Wang, Bo Li +5
Due to its robust and precise distance measurements, LiDAR plays an important role in scene understanding for autonomous driving. Training deep neural networks (DNNs) on LiDAR data…
cs.CV2020★ 36 cited
Rethinking Distributional Matching Based Domain Adaptation
Bo Li, Yezhen Wang, Tong Che +6
Domain adaptation (DA) is a technique that transfers predictive models trained on a labeled source domain to an unlabeled target domain, with the core difficulty of resolving distr…