12 citations · 12 across the 7 of their papers we have counts for
9 papers
Sample-wise Adaptive Weighting for Transfer Consistency in Adversarial Distillation
Hongsin Lee, Hye Won Chung
Adversarial distillation in the standard min-max adversarial training framework aims to transfer adversarial robustness from a large, robust teacher network to a compact student. H…
SNAP: Low-Latency Test-Time Adaptation with Sparse Updates
Hyeongheon Cha, Dong Min Kim, Hye Won Chung +2
Test-Time Adaptation (TTA) adjusts models using unlabeled test data to handle dynamic distribution shifts. However, existing methods rely on frequent adaptation and high computatio…
CovMatch: Cross-Covariance Guided Multimodal Dataset Distillation with Trainable Text Encoder
Yongmin Lee, Hye Won Chung
Multimodal dataset distillation aims to synthesize a small set of image-text pairs that enables efficient training of large-scale vision-language models. While dataset distillation…
VIPAMIN: Visual Prompt Initialization via Embedding Selection and Subspace Expansion
Jaekyun Park, Hye Won Chung
In the era of large-scale foundation models, fully fine-tuning pretrained networks for each downstream task is often prohibitively resource-intensive. Prompt tuning offers a lightw…
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…
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…