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| Survey of Metric-Based Few-Shot Classification |
| LIU Xin1,2, ZHOU Kairui1,2, He Yulin3, JING Liping1,2, YU Jian1,2 |
1. Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044
2. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044
3. Beijing Newlink Technology Co., Ltd., Beijing 100083 |
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Abstract Few-shot learning aims to make machines recognize and summarize things by learning from a small number of samples like humans. The metric-based few-shot learning method is designed to learn a low-dimensional embedding space and query samples can be classified based on a distance between the query samples and the class embeddings in this space. In this paper, the key issues, class representation learning and similarity learning , are discussed to sort out the relevant literature. Only metric-based few-shot learning methods are classified in a detailed and comprehensive way, and they are classified from the perspective of key issues. Finally, the experimental results of current representative research on commonly used image classification datasets are summarized, the problems of the existing methods are analyzed, and the future research is prospected.
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Received: 28 April 2021
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| Fund:National Natural Science Foundation of China(No.61632004), Fundamental Research Funds for Graduate Student Innovation Project(No.2021YJS031), Beijing Natural Science Foundation(No.Z180006), Fundamental Research Funds for the Central Universities(No.2019JBZ110) |
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Corresponding Authors:
JING Liping, Ph.D., professor. Her research interests include machine learning, high-dimensional representation lear-ning and its application in artificial intelligence fields.
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About author:: LIU Xin, Ph.D. candidate. Her research interests include machine learning, metric learning and few-shot learning.
ZHOU Kairui, master student. His research interests include machine learning and few-shot learning.
HE Yulin, Ph.D. His research interests include enterprise architecture design, high-performance distributed trading system, offline and real-time big data analysis.
YU Jian, Ph.D., professor. His research interests include artificial intelligence and machine learning. |
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2021, Vol. 34 
