73 citations · 104 across the 5 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.LG2020★ 73 cited
Multi-source Domain Adaptation in the Deep Learning Era: A Systematic Survey
Sicheng Zhao, Bo Li, Colorado Reed +2
In many practical applications, it is often difficult and expensive to obtain enough large-scale labeled data to train deep neural networks to their full capability. Therefore, tra…