文摘
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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
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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
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电子学报
,2022,50(2):295-304 【核心库】
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DOI
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10.12263/DZXB.20210453
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关键词
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小样本图像
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传递迁移学习
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元学习
;
元损失函数
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地址
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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
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语种
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中文 |
文献类型
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研究性论文 |
ISSN
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0372-2112 |
学科
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自动化技术、计算机技术 |
基金
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国家自然科学基金面上项目
;
湖北省重点研发计划
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文献收藏号
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CSCD:7195140
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