22 citations · 24 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 1 cited
Removing Undesirable Feature Contributions Using Out-of-Distribution Data
Saehyung Lee, Changhwa Park, Hyungyu Lee +3
Several data augmentation methods deploy unlabeled-in-distribution (UID) data to bridge the gap between the training and inference of neural networks. However, these methods have c…
cs.CV2020★ 22 cited
Joint Contrastive Learning for Unsupervised Domain Adaptation
Changhwa Park, Jonghyun Lee, Jaeyoon Yoo +2
Enhancing feature transferability by matching marginal distributions has led to improvements in domain adaptation, although this is at the expense of feature discrimination. In par…
cs.LG2019★ 1 cited
Learning Condensed and Aligned Features for Unsupervised Domain Adaptation Using Label Propagation
Jaeyoon Yoo, Changhwa Park, Yongjun Hong +1
Unsupervised domain adaptation aiming to learn a specific task for one domain using another domain data has emerged to address the labeling issue in supervised learning, especially…