no code implementations • 21 Mar 2023 • DongGyu Lee, Sangwon Jung, Taesup Moon
Specifically, we first show through two-task CL experiments that standard CL methods, which are unaware of dataset bias, can transfer biases from one task to another, both forward and backward, and this transfer is exacerbated depending on whether the CL methods focus on the stability or the plasticity.
no code implementations • 1 Mar 2023 • Sangwon Jung, TaeEon Park, Sanghyuk Chun, Taesup Moon
Many existing group fairness-aware training methods aim to achieve the group fairness by either re-weighting underrepresented groups based on certain rules or using weakly approximated surrogates for the fairness metrics in the objective as regularization terms.
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.