145 citations · 147 across the 3 of their papers we have counts for
3 papers
cs.CV2022
Unsupervised Domain Adaptation Based on the Predictive Uncertainty of Models
JoonHo Lee, Gyemin Lee
Unsupervised domain adaptation (UDA) aims to improve the prediction performance in the target domain under distribution shifts from the source domain. The key principle of UDA is t…
cs.CV2022★ 2 cited
Feature Alignment by Uncertainty and Self-Training for Source-Free Unsupervised Domain Adaptation
JoonHo Lee, Gyemin Lee
Most unsupervised domain adaptation (UDA) methods assume that labeled source images are available during model adaptation. However, this assumption is often infeasible owing to con…
stat.ML2017★ 145 cited
Domain Generalization by Marginal Transfer Learning
Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan +2
In the problem of domain generalization (DG), there are labeled training data sets from several related prediction problems, and the goal is to make accurate predictions on future…