测试科学与仪器2026，Vol.17Issue(2)：331-343,13.DOI:10.62756/jmsi.1674-8042.2026028
基于自适应原型与语义感知的长尾表征学习算法
Long-tailed representation learning algorithm based on adaptive prototypes and semantic awareness
摘要
Abstract
Label scarcity and long-tailed distribution imbalance are significant challenges in industrial equipment monitoring.Currently,self-supervised learning methods are affected by sample quantity bias and semantic confusion under complex operating conditions,which limits their ability to represent sparse critical states.To address these issues,we propose a co-evolutionary prototypical contrastive learning(EPCL)framework.Through progressive learning from coarse-grained semantic discovery to fine-grained discriminative enhancement,this framework enables an in-depth analysis of the intrinsic structure of long-tailed data.Specifically,an adaptive prototype-based clustering algorithm based on optimal transport theory is introduced,thereby achieving unbiased representation learning through data-driven dynamic priors.Furthermore,a semantic-aware and hierarchical negative sample weighting scheme is designed to optimize discriminative boundaries while mitigating class imbalance by enforcing prototype consistency constraints and employing an adaptive weighting strategy.Extensive experiments were conducted on several public long-tailed visual benchmarks,including CIFAR10-LT,CIFAR100-LT,and ImageNet-100-LT,as well as the industrial fault diagnosis dataset.The results demonstrated that the EPCL achieved better performance than fifteen mainstream self-supervised methods(e.g.,SimCLR and SwAV)in both linear evaluation and few-shot classification tasks.On the CIFAR100-LT dataset,the EPCL improved the tail-class accuracy by 4.56%compared to SimCLR.Ablation studies and visualization results verified the effectiveness and generalization ability of the framework.This work offers a promising insight and practical solution for representation learning from unlabeled long-tailed measurement data.关键词
长尾分布/自监督学习/对比学习/语义感知/自适应原型聚类/表征学习Key words
long-tailed distribution/self-supervised learning/contrastive learning/semantic awareness/adaptive prototype clustering/representation learning引用本文复制引用
李甜甜,薛震,张亮亮,连旭..基于自适应原型与语义感知的长尾表征学习算法[J].测试科学与仪器,2026,17(2):331-343,13.基金项目
The work was supported by the National Natural Science Foundation of China(No.12401703)and the Fundamental Research Program of Shanxi Province(Nos.202203021211088,202403021221109,202403021212256). （No.12401703）
