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cs.LG2026
Quantum-Gated Task-interaction Knowledge Distillation for Pre-trained Model-based Class-Incremental Learning
Linjie Li, Huiyu Xiao, Jiarui Cao +2
Class-incremental learning (CIL) aims to continuously accumulate knowledge from a stream of tasks and construct a unified classifier over all seen classes. Although pretrained mode…
cs.LG2025
MoTE: Mixture of Task-specific Experts for Pre-Trained ModelBased Class-incremental Learning
Linjie Li, Zhenyu Wu, Yang Ji
Class-incremental learning (CIL) requires deep learning models to continuously acquire new knowledge from streaming data while preserving previously learned information. Recently,…
cs.LG2018
An Adaptive Oversampling Learning Method for Class-Imbalanced Fault Diagnostics and Prognostics
Wenfang Lin, Zhenyu Wu, Yang Ji
Data-driven fault diagnostics and prognostics suffers from class-imbalance problem in industrial systems and it raises challenges to common machine learning algorithms as it become…