3 papers
cs.CV2026
Non-Forgetting Knowledge Allocation with Bi-level Competition for Class-Incremental Learning
Xiang Tan, Run He, Yawen Cui +6
Class-Incremental Learning (CIL) with pre-trained models (PTMs) aims to sequentially adapt PTMs to new categories without forgetting old knowledge. Built upon PTMs, existing adapte…
cs.LG2026
Model-agnostic Selective Labeling with Provable Statistical Guarantees
Huipeng Huang, Wenbo Liao, Huajun Xi +3
Obtaining high-quality labels for large datasets is expensive, requiring massive annotations from human experts. While AI models offer a cost-effective alternative by predicting la…
cs.CL2025
GeRe: Towards Efficient Anti-Forgetting in Continual Learning of LLM via General Samples Replay
Yunan Zhang, Shuoran Jiang, Mengchen Zhao +4
The continual learning capability of large language models (LLMs) is crucial for advancing artificial general intelligence. However, continual fine-tuning LLMs across various domai…