电子学报 ›› 2022, Vol. 50 ›› Issue (2): 295-304.DOI: 10.12263/DZXB.20210453

• 学术论文 • 上一篇    下一篇

基于样本对元学习的小样本图像分类方法

李维刚, 甘平, 谢璐, 李松涛   

  1. 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
  • 收稿日期:2021-04-09 修回日期:2021-10-31 出版日期:2022-02-25 发布日期:2022-02-25
  • 作者简介:李维刚 男,1977年生,湖北通城人.博士、教授、博导,主要研究方向为人工智能与机器学习算法.
    甘 平(通讯作者) 男,1995年生,湖北武汉人.硕士,主要研究方向为小样本学习、GAN.E-mail: 383615194@qq.com
  • 基金资助:
    国家自然科学基金面上项目(51774219);湖北省重点研发计划(2020BAB098)

A Few-Shot Image Classification Method by Pairwise-Based Meta Learning

LI Wei-gang, GAN Ping, XIE Lu, LI Song-tao   

  1. Engineering Research Center for Metallurgical Automation and Measurement Technology(Ministry of Education),Wuhan University ofScience and Technology,Wuhan,Hubei 430081,China
  • Received:2021-04-09 Revised:2021-10-31 Online:2022-02-25 Published:2022-02-25

摘要:

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%.

关键词: 小样本图像, 传递迁移学习, 元学习, 元损失函数

Abstract:

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.

Key words: few-shot image, transitive transfer learning, meta learning, meta loss function

中图分类号: