58 citations · 325 across the 29 of their papers we have counts for
13 papers · 1 filter
DaWin: Training-free Dynamic Weight Interpolation for Robust Adaptation
Changdae Oh, Yixuan Li, Kyungwoo Song +2
Adapting a pre-trained foundation model on downstream tasks should ensure robustness against distribution shifts without the need to retrain the whole model. Although existing weig…
Model Stock: All we need is just a few fine-tuned models
Dong-Hwan Jang, Sangdoo Yun, Dongyoon Han
This paper introduces an efficient fine-tuning method for large pre-trained models, offering strong in-distribution (ID) and out-of-distribution (OOD) performance. Breaking away fr…
Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data
Jang-Hyun Kim, Sangdoo Yun, Hyun Oh Song
Diagnosing and cleaning data is a crucial step for building robust machine learning systems. However, identifying problems within large-scale datasets with real-world distributions…
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective
Chanwoo Park, Sangdoo Yun, Sanghyuk Chun
We propose the first unified theoretical analysis of mixed sample data augmentation (MSDA), such as Mixup and CutMix. Our theoretical results show that regardless of the choice of…
Dataset Condensation via Efficient Synthetic-Data Parameterization
Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh +5
The great success of machine learning with massive amounts of data comes at a price of huge computation costs and storage for training and tuning. Recent studies on dataset condens…
OCR-free Document Understanding Transformer
Geewook Kim, Teakgyu Hong, Moonbin Yim +7
Understanding document images (e.g., invoices) is a core but challenging task since it requires complex functions such as reading text and a holistic understanding of the document.…