12 citations · 12 across the 7 of their papers we have counts for
4 papers · 1 filter
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…
Representation Norm Amplification for Out-of-Distribution Detection in Long-Tail Learning
Dong Geun Shin, Hye Won Chung
Detecting out-of-distribution (OOD) samples is a critical task for reliable machine learning. However, it becomes particularly challenging when the models are trained on long-taile…
BWS: Best Window Selection Based on Sample Scores for Data Pruning across Broad Ranges
Hoyong Choi, Nohyun Ki, Hye Won Chung
Data subset selection aims to find a smaller yet informative subset of a large dataset that can approximate the full-dataset training, addressing challenges associated with trainin…
SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matching
Yongmin Lee, Hye Won Chung
Dataset distillation aims to synthesize a small number of images per class (IPC) from a large dataset to approximate full dataset training with minimal performance loss. While effe…