基于样本对元学习的小样本图像分类方法
A Few-Shot Image Classification Method by Pairwise-Based Meta Learning

李维刚甘平 ^* 谢璐李松涛

文摘	本文针对小样本图像分类问题,提出一种基于样本对的元学习(Pairwise-based Meta Learning,PML)方法.利用传递迁移学习对预训练好的Resnet50模型进行微调,得到一个更适应小样本任务的特征编码器,将该特征编码器作为元学习模型的初始特征编码器来训练模型,进一步增强了元学习模型的泛化能力;同时,本文还基于支持集与查询集样本之间的相似性提出元损失函数(Meta Loss,ML),其考虑了特征空间中查询集所有样本的相互关系,以此来缩小正样本类内距离,增加正负样本类间距离,从而提高分类精度.实验结果表明,本文的方法在1-shot、5-shot任务上分别达到了77.65%、89.65%的分类精度,较最新的元学习方法Meta-baseline分别提高7.38%、5.65%.
其他语种文摘	In this paper, a pairwise-based meta learning(PML) method is proposed for few-shot image classification. Transitive transfer learning is used to fine tune the pre-trained Resnet50 model to get a feature encoder that is more suitable for few shot task. The feature encoder is used as the initial feature encoder of the meta-learning model to train the model, which further enhances the generalization ability of the meta-learning model. Based on the similarity between the support set and the query set samples, a meta loss(ML) function is proposed, which considers the relationship between all the samples of the query set in the feature space, so as to reduce the within-class distance of positive samples and increase the between-class distance of positive and negative samples, thus improving the classification accuracy.The experimental results show that the classification accuracy of the methods in this paper is 77.65% and 89.65% on 1-shot and 5-shot tasks, respectively, and it is 7.38% and 5.65% higher than the latest meta-learning method, Meta-baseline.
来源	电子学报 ,2022,50(2):295-304 【核心库】
DOI	10.12263/DZXB.20210453
关键词	小样本图像 ; 传递迁移学习 ; 元学习 ; 元损失函数
地址	武汉科技大学, 冶金自动化与检测技术教育部工程研究中心, 湖北, 武汉, 430081
语种	中文
文献类型	研究性论文
ISSN	0372-2112
学科	自动化技术、计算机技术
基金	国家自然科学基金面上项目 ; 湖北省重点研发计划
文献收藏号	CSCD:7195140