22 citations · 24 across the 3 of their papers we have counts for
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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.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…