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    • Class Representation Networks for Few-Shot Learning

    Yongping Zhai,Junhua Wang

    International Conference on Software Engineering (ICSE)（2020）CCF A

    Natl Univ Def Technol

    Cited 0|Views7
    Abstract
    In this paper, we proposed a novel network referred as Class Representation Networks (CRNs) to solve the few-shot learning problems. In the proposed CRNs, a high-quality class representation is learned by training a set-based neural network. In addition, a network with fully connected layers was constructed for learning distance metric instead of using a predefined distance metric. Compared with recent methods for few-shot learning, our network achieves state-of-the-art performance for few-shot learning. Extensive experiments on three benchmark datasets validate the effectiveness of our proposed model.
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    Key words
    few-shot,metric learning,deep learning,meta-learning
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    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
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    【要点】：本文提出了一种名为类表示网络（CRNs）的新型网络，通过训练基于集合的神经网络学习高质量类表示，以及通过全连接层网络学习距离度量，有效解决小样本学习问题，实现当前最佳性能。
    

    【方法】：通过构建类表示网络（CRNs），使用基于集合的神经网络学习类表示，并构建全连接层网络来自动学习距离度量。
    

    【实验】：在三个基准数据集上进行了大量实验，验证了所提模型的有效性，数据集名称未在摘要中明确提及。
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