no code implementations • 31 Jan 2024 • Kyungsung Lee, DongGyu Lee, Myungjoo Kang
We comprehensively evaluate the performance of our model on a variety of noisy inverse problems, including inpainting, denoising, and super-resolution.
1 code implementation • NeurIPS 2023 • Peter Yongho Kim, Junbeom Kwon, Sunghwan Joo, Sangyoon Bae, DongGyu Lee, Yoonho Jung, Shinjae Yoo, Jiook Cha, Taesup Moon
To address this challenge, we present SwiFT (Swin 4D fMRI Transformer), a Swin Transformer architecture that can learn brain dynamics directly from fMRI volumes in a memory and computation-efficient manner.
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.
2 code implementations • 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)