113 citations · 203 across the 21 of their papers we have counts for
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cs.LG2024
Towards Robust and Parameter-Efficient Knowledge Unlearning for LLMs
Sungmin Cha, Sungjun Cho, Dasol Hwang +1
Large Language Models (LLMs) have demonstrated strong reasoning and memorization capabilities via pretraining on massive textual corpora. However, this poses risk of privacy and co…
cs.CV2024
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…
cs.LG2024★ 1 cited
Hyperparameters in Continual Learning: A Reality Check
Sungmin Cha, Kyunghyun Cho
Continual learning (CL) aims to train a model on a sequence of tasks (i.e., a CL scenario) while balancing the trade-off between plasticity (learning new tasks) and stability (reta…