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 • 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.
1 code implementation • NeurIPS 2019 • Hongjoon Ahn, Sungmin Cha, DongGyu Lee, Taesup Moon
We introduce a new neural network-based continual learning algorithm, dubbed as Uncertainty-regularized Continual Learning (UCL), which builds on traditional Bayesian online learning framework with variational inference.
Ranked #11 on Continual Learning on ASC (19 tasks)