2 code implementations • 7 Feb 2022 • Saehyung Lee, Sanghyuk Chun, Sangwon Jung, Sangdoo Yun, Sungroh Yoon
However, in this study, we prove that the existing DC methods can perform worse than the random selection method when task-irrelevant information forms a significant part of the training dataset.
1 code implementation • CVPR 2022 • Sangwon Jung, Sanghyuk Chun, Taesup Moon
To address this problem, we propose a simple Confidence-based Group Label assignment (CGL) strategy that is readily applicable to any fairness-aware learning method.
no code implementations • CVPR 2021 • Sangwon Jung, DongGyu Lee, TaeEon Park, Taesup Moon
Fairness is becoming an increasingly crucial issue for computer vision, especially in the human-related decision systems.
no code implementations • NeurIPS 2020 • Sangwon Jung, Hongjoon Ahn, Sungmin Cha, Taesup Moon
We propose a novel regularization-based continual learning method, dubbed as Adaptive Group Sparsity based Continual Learning (AGS-CL), using two group sparsity-based penalties.