Better to Follow, Follow to be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection J Noh, W Bae, W Lee, J Seo, G Kim International Conference on Computer Vision (ICCV), 9725-9734, 2019 | 252 | 2019 |
Rethinking Class Activation Mapping for Weakly Supervised Object Localization W Bae*, J Noh*, G Kim European Conference on Computer Vision (ECCV), 618-634, 2020 | 132 | 2020 |
Object Discovery via Contrastive Learning for Weakly Supervised Object Detection J Seo, W Bae, DJ Sutherland, J Noh*, D Kim* European Conference on Computer Vision (ECCV), 312-329, 2022 | 36 | 2022 |
Prediction of the mortality risk in peritoneal dialysis patients using machine learning models: a nation-wide prospective cohort in Korea J Noh, KD Yoo, W Bae, JS Lee, K Kim, JH Cho, H Lee, DK Kim, CS Lim, ... Scientific reports 10 (1), 7470, 2020 | 31 | 2020 |
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels MA Mohamadi*, W Bae*, DJ Sutherland Neural Information Processing Systems (NeurIPS), 2022 | 28 | 2022 |
A fast, well-founded approximation to the empirical neural tangent kernel MA Mohamadi, W Bae, DJ Sutherland International Conference on Machine Learning (ICML), 25061-25081, 2023 | 21 | 2023 |
Meta Temporal Point Processes W Bae, MO Ahmed, F Tung, GL Oliveira International Conference on Learning Representations (ICLR), 2023 | 18 | 2023 |
How to prepare your task head for finetuning Y Ren, S Guo, W Bae, DJ Sutherland International Conference on Learning Representations (ICLR), 2023 | 16 | 2023 |
One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model W Bae, J Noh, MJ Asadabadi, DJ Sutherland International Joint Conference on Artificial Intelligence (IJCAI), 2022 | 5 | 2022 |
Predicting outcomes of continuous renal replacement therapy using body composition monitoring: a deep-learning approach KD Yoo, J Noh, W Bae, JN An, HJ Oh, H Rhee, EY Seong, SH Baek, ... Scientific Reports 13 (1), 4605, 2023 | 4 | 2023 |
LID 2020: The learning from imperfect data challenge results Y Wei, S Zheng, MM Cheng, H Zhao, L Wang, E Ding, Y Yang, A Torralba, ... The 2020 Learning from Imperfect Data (LID) Challenge - CVPR Workshop, 2020 | 3 | 2020 |
Revisiting Class Activation Mapping for Learning from Imperfect Data W Bae, J Noh, J Seo, G Kim The 2020 Learning from Imperfect Data (LID) Challenge - CVPR Workshop, 2020 | 2 | 2020 |
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling W Bae, J Wang, DJ Sutherland European Conference on Computer Vision (ECCV), 2024 | 1 | 2024 |
What Has Been Overlooked in Contrastive Source-Free Domain Adaptation: Leveraging Source-Informed Latent Augmentation within Neighborhood Context J Wang, W Bae, J Chen, K Zhang, L Sigal, CW de Silva Transactions on Machine Learning Research (TMLR), 2024 | 1 | 2024 |
AdaFlood: Adaptive Flood Regularization W Bae, Y Ren, MO Ahmed, F Tung, DJ Sutherland, GL Oliveira Transactions on Machine Learning Research (TMLR), 2023 | 1 | 2023 |
Uncertainty Herding: One Active Learning Method for All Label Budgets W Bae, GL Oliveira, DJ Sutherland arXiv preprint arXiv:2412.20644, 2024 | | 2024 |
Predicting early mortality in hemodialysis patients: a deep learning approach using a nationwide prospective cohort in South Korea J Noh, SY Park, W Bae, K Kim, JH Cho, JS Lee, SW Kang, YL Kim, ... Scientific reports 14 (1), 29658, 2024 | | 2024 |
Generalized Coverage for More Robust Low-Budget Active Learning W Bae, J Noh, DJ Sutherland European Conference on Computer Vision (ECCV), 2024 | | 2024 |
Towards Explainable Computer Vision Methods via Uncertainty Activation Map S Shin, W Bae, J Noh, S Choi Asian Conference on Pattern Recognition (ACPR), 1-14, 2023 | | 2023 |