12 citations · 12 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation
Minguk Jang, Hye Won Chung
Test-time adaptation (TTA) is an effective approach to mitigate performance degradation of trained models when encountering input distribution shifts at test time. However, existin…
cs.LG2022
Few-Example Clustering via Contrastive Learning
Minguk Jang, Sae-Young Chung
We propose Few-Example Clustering (FEC), a novel algorithm that performs contrastive learning to cluster few examples. Our method is composed of the following three steps: (1) gene…
cs.CV2022★ 12 cited
Test-Time Adaptation via Self-Training with Nearest Neighbor Information
Minguk Jang, Sae-Young Chung, Hye Won Chung
Test-time adaptation (TTA) aims to adapt a trained classifier using online unlabeled test data only, without any information related to the training procedure. Most existing TTA me…