[11/2020] I joined Lawrence Livermore National Laboratory (LLNL) as a postdoctoral researcher.
[08/2020] I was recognized as an outstanding reviewer of ECCV 2020.
[07/2020] Our paper on Charlson Comorbidity Index (CCI) is accepted at Scientific Reports.
[07/2020] Our paper on weakly supervised object localization is accepted at ECCV 2020.
[04/2020] Our paper on peritoneal dialysis is accepted at Scientific Reports.
[02/2020] I received SNU CSE best thesis award.
[07/2019] Our paper on small object detection is accepted at ICCV 2019.
Thesis: Improving Object Detection in Hard Conditions of Scale, Occlusion and Label
Thesis: Machine Learning Models and Missing Data Imputation Methods in Predicting the Progression of IgA Nephropathy
Thesis: Prediction of Customer’s Follow-on Purchase using Ensemble Methods
Junhyug Noh*, Wonho Bae*, and Gunhee Kim. Rethinking Class Activation Mapping for Weakly Supervised Object Localization. ECCV 2020.
Junhyug Noh*, Kyung Don Yoo*, Wonho Bae, …, and Jung Pyo Lee. Prediction of the Mortality Risk in Peritoneal Dialysis Patients using Machine Learning Models: A Nation-wide Prospective Cohort in Korea. Scientific Reports 2020.
Junhyug Noh, Wonho Bae, Wonhee Lee, Jinhwan Seo, and Gunhee Kim. Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection. ICCV 2019.
Kangil Kim, Junhyug Noh, Dong-Kyun Kim, and Minhyeok Kim. Conflict Relaxation of Activation-Based Regularization for Neural Network, IEEE Access 2018.
Junhyug Noh, Soochan Lee, Beomsu Kim, and Gunhee Kim. Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors. CVPR 2018.
Junhyug Noh*, Kyung Don Yoo*, Hajeong Lee, …, and Yon Su Kim. A Machine Learning Approach Using Survival Statistics to Predict Graft Survival in Kidney Transplant Recipients: A Multicenter Cohort Study. Scientific Reports 2017.