改进的基于Few-Shot学习的图像分类

2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS) Pub Date : 2022-07-22 DOI:10.1109/ISPDS56360.2022.9874232

Jialin Yu, Jun Liang, Haoyang Mei, Jingwen Fan, Songsen Yu

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引用次数: 0

摘要

少射学习是一种用有限的标记样本对看不见的类进行分类的方法。我们提出了改进的关系网络网络对小样本图像进行分类。改进后的网络是ECA关系网络(ECA- rnet)。在mini-ImageNet数据集的5-way 1-shot和5-way 5-shot上，ECA-RNET的准确率分别为52.24%和67.85%。

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Improved Few-Shot Learning for Images Classification

Few-shot learning is an approach that classify unseen classes with limited labeled samples. We propose improved networks of Relation Network to classify images with small samples. The improved networks is ECA Relation Network (ECA-RNET). The accuracy of ECA-RNET is 52.24% and 67.85% on 5-way 1-shot and 5-way 5-shot of mini-ImageNet dataset, respectively.

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来源期刊

2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)

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