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    杨洁祎, 董一鸿, 钱江波. 基于图神经网络的小样本学习方法研究进展[J]. 计算机研究与发展. DOI: 10.7544/issn1000-1239.202220933
    引用本文: 杨洁祎, 董一鸿, 钱江波. 基于图神经网络的小样本学习方法研究进展[J]. 计算机研究与发展. DOI: 10.7544/issn1000-1239.202220933
    Yang Jieyi, Dong Yihong, Qian Jiangbo. Research Progress of Few-Shot Learning Methods Based on Graph Neural Networks[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202220933
    Citation: Yang Jieyi, Dong Yihong, Qian Jiangbo. Research Progress of Few-Shot Learning Methods Based on Graph Neural Networks[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202220933

    基于图神经网络的小样本学习方法研究进展

    Research Progress of Few-Shot Learning Methods Based on Graph Neural Networks

    • 摘要: 小样本学习旨在利用少量样本学习得到解决问题的模型,为解决应用场景中样本量少或标注样本少的问题. 图神经网络由于其在许多应用中的卓越性能引起了极大的关注,许多学者开始尝试利用图神经网络进行小样本学习,基于图神经网络的方法在小样本领域取得了卓越的成绩. 目前与基于图神经网络的小样本学习方法相关的综述性研究较少,缺乏该类方法的划分体系与介绍性工作,因此系统地梳理了当前基于图神经网络的小样本学习的相关工作:概括了小样本学习的图神经网络方法的概念,根据模型的基本思想将其划分为基于节点特征、基于边特征、基于节点对特征和基于类级特征4类方法,介绍了这4类方法的研究进展;总结了目前常用的小样本数据集和代表性模型在这些数据集上的实验结果,归纳各类方法主要的研究内容和优劣势;最后概述了基于图神经网络的小样本学习方法的应用和面临的

       

      Abstract: Few-shot learning(FSL) aims to learn to get a problem-solving model using a small number of samples. Under the trend of training models with big data, deep learning has gained success in many fields, but realistic scenarios often lack sufficient samples or labeled samples. Therefore FSL becomes a promising research direction at present. Graph neural networks(GNN) have attracted great attention due to their excellent performance in many applications. In view of this, many methods try to use GNN for FSL. Currently there are few review researches related to FSL methods based on GNN, and there is a lack of division system and introductory work on this type of methods. This paper systematically composes the current work related to FSL based on GNN. It outlines the basis and concepts of graph methods for FSL, broadly classifies them into four categories of methods based on node-based feature, edge-based feature, node-pair-based feature and class-level-based feature according to the basic ideas of the models. The research progress of them is introduced as well. Then the experimental results of the commonly used few-shot datasets and representative models on these datasets are summarized, as well as the advantages and disadvantages of each type of methods. Finally, current status and challenges of the graph methods for FSL are induced, and its future directions are prospected.

       

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