Survey
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 10
2881-2888,2895

Survey on few-shot learning for deep network

Pan Xueling1a,1b
Li Guohe1a,1b
Zheng Yifeng2,3
1. a. Beijing Key Lab of Petroleum Data Mining, b. College of Information Science & Engineering, China University of Petroleum, Beijing 102249, China
2. School of Computer Science, Minnan Normal University, Zhangzhou Fujian 363000, China
3. Key Laboratory of Data Science & Intelligence Application, Fujian Province University, Zhangzhou Fujian 363000, China
DOI: 10.19734/j.issn.1001-3695.2023.02.0074

Abstract

Deep learning is widely used in various fields, which is data-driven. Data privacy and expensive data labeling or other questions cause samples absent and data incompleteness. Moreover, small samples cannot accurately represent data distribution, reducing classification performance and generalization ability. Therefore, few-shot learning is defined to achieve fast learning by utilizing a small target samples. This paper systematically summarized the current approaches of few-shot learning, introducing models from the three categories: data augmentation-based, meta-learning based, and transduction graph-based. First, it illustrated the data augmentation-based approaches according to supervised and unsupervised augmentations. Then, it analyzed the meta-learning based approaches from metric learning and parameter optimization. Next, it elaborated transduction graph-based approaches. Eventually, it introduced the commonly few-shot datasets, and analyzed representative few-shot learning models through experiments. In addition, this paper summarized the challenges and the future technological development of few-shot learning.

Foundation Support

国家自然科学基金资助项目(62376114)
克拉玛依科技发展计划项目(2020CGZH0009)
福建省自然科学基金资助项目(2021J011004,2021J011002)
教育部产学研创新计划资助项目(2021LDA09003)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.02.0074
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 10
Section: Survey
Pages: 2881-2888,2895
Serial Number: 1001-3695(2023)10-001-2881-08

Publish History

[2023-04-28] Accepted Paper
[2023-10-05] Printed Article

Cite This Article

潘雪玲, 李国和, 郑艺峰. 面向深度网络的小样本学习综述 [J]. 计算机应用研究, 2023, 40 (10): 2881-2888,2895. (Pan Xueling, Li Guohe, Zheng Yifeng. Survey on few-shot learning for deep network [J]. Application Research of Computers, 2023, 40 (10): 2881-2888,2895. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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