88 citations · 179 across the 11 of their papers we have counts for
6 papers · 1 filter
CLARE: Scalable Class-Incremental Continual Learning via a Sparsity-Based Framework
Yunxiang Fu, Meng Lou, Zicheng Liao +1
Continual learning must balance the learning of new knowledge with the retention of previously learned knowledge to incrementally learn tasks from a data stream without catastrophi…
One Adapter, Many Tasks: Task-Conditioned Feature Transformations for Continual Learning
Yunxiang Fu, Meng Lou, Yizhou Yu
Class-incremental learning (CIL) requires a model to incrementally learn tasks that contain new classes without accessing earlier training data while preserving the ability to reco…
Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning
Meng Lou, Hanzhong Guo, Linwei Chen +1
Recent studies suggest that Reinforcement Fine-Tuning (RFT) is inherently more resilient to catastrophic forgetting than Supervised Fine-Tuning (SFT). However, whether RFT (e.g., G…
CGSA: Class-Guided Slot-Aware Adaptation for Source-Free Object Detection
Boyang Dai, Zeng Fan, Zihao Qi +2
Source-Free Domain Adaptive Object Detection (SF-DAOD) aims to adapt a detector trained on a labeled source domain to an unlabeled target domain without retaining any source data.…
Parameters as Experts: Adapting Vision Models with Dynamic Parameter Routing
Meng Lou, Stanley Yu, Yizhou Yu
Adapting pre-trained vision models using parameter-efficient fine-tuning (PEFT) remains challenging, as it aims to achieve performance comparable to full fine-tuning using a minima…
Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts
Meng Lou, Yunxiang Fu, Yizhou Yu
Continual learning, especially class-incremental learning (CIL), on the basis of a pre-trained model (PTM) has garnered substantial research interest in recent years. However, how…