4 papers
GroupCoOp: Group-robust Fine-tuning via Group Prompt Learning
Nayeong Kim, Seong Joon Oh, Suha Kwak
Parameter-efficient fine-tuning (PEFT) of vision-language models (VLMs) excels in various vision tasks thanks to the rich knowledge and generalization ability of VLMs. However, rec…
TestDG: Test-time Domain Generalization for Continual Test-time Adaptation
Sohyun Lee, Nayeong Kim, Juwon Kang +2
This paper studies continual test-time adaptation (CTTA), the task of adapting a model to constantly changing unseen domains in testing while preserving previously learned knowledg…
Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization
Nayeong Kim, Juwon Kang, Sungsoo Ahn +2
We study the problem of training an unbiased and accurate model given a dataset with multiple biases. This problem is challenging since the multiple biases cause multiple undesirab…
SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems
Moon Ye-Bin, Nam Hyeon-Woo, Wonseok Choi +3
Data imbalance in training data often leads to biased predictions from trained models, which in turn causes ethical and social issues. A straightforward solution is to carefully cu…