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

doi:10.12263/DZXB.20210453

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基于样本对元学习的小样本图像分类方法

学术论文 | 更新时间：2025-12-08

- 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
- 电子学报 2022年50卷第2期页码：295-304
- 作者机构：
  
  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
- 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" ]
- 基金信息：
  
  国家自然科学基金面上项目(51774219);湖北省重点研发计划(2020BAB098)
- DOI：10.12263/DZXB.20210453
  中图分类号： TP391.4
- 收稿：2021-04-09，
  
  修回：2021-10-31，
  
  纸质出版：2022-02-25
- 稿件说明：
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李维刚,甘平,谢璐等.基于样本对元学习的小样本图像分类方法[J].电子学报,2022,50(02):295-304.

LI Wei-gang,GAN Ping,XIE Lu,et al.A Few-Shot Image Classification Method by Pairwise-Based Meta Learning[J].ACTA ELECTRONICA SINICA,2022,50(02):295-304.
李维刚,甘平,谢璐等.基于样本对元学习的小样本图像分类方法[J].电子学报,2022,50(02):295-304. DOI： 10.12263/DZXB.20210453.

LI Wei-gang,GAN Ping,XIE Lu,et al.A Few-Shot Image Classification Method by Pairwise-Based Meta Learning[J].ACTA ELECTRONICA SINICA,2022,50(02):295-304. DOI： 10.12263/DZXB.20210453.

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%.

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.

关键词

Keywords

references

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