858 citations · 860 across the 5 of their papers we have counts for
5 papers
Improving Instruction Following in Language Models through Proxy-Based Uncertainty Estimation
JoonHo Lee, Jae Oh Woo, Juree Seok +11
Assessing response quality to instructions in language models is vital but challenging due to the complexity of human language across different contexts. This complexity often resu…
Unsupervised Accuracy Estimation of Deep Visual Models using Domain-Adaptive Adversarial Perturbation without Source Samples
JoonHo Lee, Jae Oh Woo, Hankyu Moon +1
Deploying deep visual models can lead to performance drops due to the discrepancies between source and target distributions. Several approaches leverage labeled source data to esti…
Unsupervised Domain Adaptation Based on the Predictive Uncertainty of Models
JoonHo Lee, Gyemin Lee
Unsupervised domain adaptation (UDA) aims to improve the prediction performance in the target domain under distribution shifts from the source domain. The key principle of UDA is t…
Feature Alignment by Uncertainty and Self-Training for Source-Free Unsupervised Domain Adaptation
JoonHo Lee, Gyemin Lee
Most unsupervised domain adaptation (UDA) methods assume that labeled source images are available during model adaptation. However, this assumption is often infeasible owing to con…
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
José Ignacio Orlando, Huazhu Fu, João Barbossa Breda +28
Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modali…