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Dynamic LoRA-Experts and Prototype-Ensemble Matching for Class-Incremental Learning
Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previously acquired knowledge. Parameter-efficient fine-tuning with pre-trained models reduces parameter overhead but can suffer from cumulative interference and suboptimal alignment between inference samples
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