计算机工程与应用2025，Vol.61Issue(6)：273-281,9.DOI:10.3778/j.issn.1002-8331.2310-0320
基于原型增强的元学习分类模型
Meta-Learning Classification Model Based on Prototype Enhanced
摘要
Abstract
Meta-learning aims to utilize existing knowledge and experience to quickly acquire new knowledge and adapt to new tasks.It is one of the commonly used methods to solve few-shot classification problems.Existing meta-learning methods usually ignore the relationship between support set and query set in feature extraction,thus failing to obtain the most discriminative features and leading to unreliable class prototypes.Therefore,this paper proposes a prototype enhanced meta-learning classification model.The model consists of two main components:a feature representation module and a prototype modification module.To address the problem of underutilization of features in existing methods,the feature representation module utilizes attention mechanism to capture the relationship between support set and query set,and update their feature representations.While to address the problem of data scarcity,the prototype modification module utilizes unlabeled samples from query set to expand the support set,and then iteratively corrects the positions of prototypes.Experimental results on mini-ImageNet and tiered-ImageNet datasets show that the classification accuracy of proposed model has significantly improved compared with other meta-learning methods.关键词
原型增强/元学习/小样本学习/图像分类Key words
prototype enhancement/meta-learning/few-shot learning/image classification分类
信息技术与安全科学引用本文复制引用
翟文茜,李凡长..基于原型增强的元学习分类模型[J].计算机工程与应用,2025,61(6):273-281,9.基金项目
国家重点研发计划(2018YFA0701700,2018YFA0701) （2018YFA0701700,2018YFA0701）
国家自然科学基金(67172364,62171672,61902269). （67172364,62171672,61902269）
