117 citations · 443 across the 36 of their papers we have counts for
40 papers
Forget Forgetting: Continual Learning in a World of Abundant Memory
Dongkyu Cho, Taesup Moon, Rumi Chunara +2
Continual learning (CL) has traditionally focused on minimizing exemplar memory, a constraint often misaligned with modern systems where GPU time, not storage, is the primary bottl…
Towards Realistic Incremental Scenario in Class Incremental Semantic Segmentation
Jihwan Kwak, Sungmin Cha, Taesup Moon
This paper addresses the unrealistic aspect of the commonly adopted Continuous Incremental Semantic Segmentation (CISS) scenario, termed overlapped. We point out that overlapped al…
SwiFT: Swin 4D fMRI Transformer
Peter Yongho Kim, Junbeom Kwon, Sunghwan Joo +6
Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches f…
Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning
Sungmin Cha, Kyunghyun Cho, Taesup Moon
We introduce a novel Pseudo-Negative Regularization (PNR) framework for effective continual self-supervised learning (CSSL). Our PNR leverages pseudo-negatives obtained through mod…
Continual Learning in the Presence of Spurious Correlation
Donggyu Lee, Sangwon Jung, Taesup Moon
Most continual learning (CL) algorithms have focused on tackling the stability-plasticity dilemma, that is, the challenge of preventing the forgetting of previous tasks while learn…
Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization
Sangwon Jung, Taeeon Park, Sanghyuk Chun +1
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 approxim…