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cs.LG2026
Dynamic LoRA-Experts and Prototype-Ensemble Matching for Class-Incremental Learning
Hongwei Zhao, Rui Liu, Yansong Liu
Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previously acquired knowledge. Parameter-efficient fine-tuning with pre-trained models re…
cs.LG2026
Decoupled and Distilled: Task-Adaptive LoRA-Teachers with Ensemble Knowledge Transfer for Few-Shot Class-Incremental Learning
Hongwei Zhao, Rui Liu, Yansong Liu +2
Few-Shot Class-Incremental Learning (FSCIL) addresses the challenge of learning new classes from very limited samples while retaining knowledge of previously learned ones. Although…
cs.LG2026
Beyond One-Size-Fits-All: Adaptive Subgraph Denoising for Zero-Shot Graph Learning with Large Language Models
Fengzhi Li, Liang Zhang, Yuan Zuo +5
Graph-based tasks in the zero-shot setting remain a significant challenge due to data scarcity and the inability of traditional Graph Neural Networks (GNNs) to generalize to unseen…