Survey
|
App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 8
2241-2247

Survey of few-shot learning based on deep neural network

Li Xinye
Long Shenpeng
Zhu Jing
Dept. of Electronics & Communication Engineering, North China Electric Power University, Baoding Hebei 071000, China
DOI: 10.19734/j.issn.1001-3695.2019.03.0036

Abstract

How to learn and identify new categories from a small number of training samples is a challenging problem for deep neural networks. For how to solve the problem of few-shot learning, this paper comprehensively summarized the existing few-shot learning methods based on deep neural networks, which covered various aspects such as models used in the methods, datasets and evaluation results. Specifically, for the few-shot learning method based on deep neural network, this paper divided it into four categories, named data enhancement method, migration learning method, metric learning method and meta-learning method. For each category, this paper further divided it into sub-categories and conducted a series of comparisons between each category and method to show the pros and cons of the various methods and their respective characteristics. Finally, the paper highlighted the limitations of existing methods and pointed to future research directions in the field of few-shot learning.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.03.0036
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 8
Section: Survey
Pages: 2241-2247
Serial Number: 1001-3695(2020)08-001-2241-07

Publish History

[2020-08-05] Printed Article

Cite This Article

李新叶, 龙慎鹏, 朱婧. 基于深度神经网络的少样本学习综述 [J]. 计算机应用研究, 2020, 37 (8): 2241-2247. (Li Xinye, Long Shenpeng, Zhu Jing. Survey of few-shot learning based on deep neural network [J]. Application Research of Computers, 2020, 37 (8): 2241-2247. )

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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)