Computer Science ›› 2022, Vol. 49 ›› Issue (5): 1-9.doi: 10.11896/jsjkx.210500128

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Survey on Few-shot Learning Algorithms for Image Classification

PENG Yun-cong1,3, QIN Xiao-lin1,2,3, ZHANG Li-ge1,3, GU Yong-xiang1,3   

  1. 1 Chengdu Institute of Computer Applications,Chinese Academy of Sciences,Chengdu 610041,China
    2 Nanchang Institute of Technology,Nanchang 330044,China
    3 School of Computer Science and Technology,University of Chinese Academy of Sciences,Beijing 100049,China
  • Received:2021-05-18 Revised:2021-10-22 Online:2022-05-15 Published:2022-05-06
  • About author:PENG Yun-cong,born in 1998,postgraduate.His main research interests include few-shot learning and theory of statistical machine learning.
    QIN Xiao-lin,born in 1980,Ph.D,professor,Ph.D supervisor.His main research interests include automatic reasoning and swarm intelligence.
  • Supported by:
    National Natural Science Foundation of China(61402537),Sichuan Science and Technology Program(2019ZDZX0005,2019ZDZX0006,2020YFQ0056,2021YFG0034),Talents by Sichuan Provincial Party Committee Organization Department and National Academy of Science Alliance Collaborative Program(Chengdu Branch of Chinese Academy of Sciences-Chongqing Academy of Science and Technology).

Abstract: Presently,artificial intelligence algorithms represented by deep learning have achieved advanced results and been successfully used in fields such as image classification,biometric recognition and medical assisted diagnosis by virtue of ultra-large-scale data sets and powerful computing resources.However,due to many restrictions in the actual environment,it is impossible to obtain a large number of samples or the cost of obtaining samples is too high.Therefore,studying the learning algorithm in the case of small samples is the core driving force to promote the intelligent process,and it has also become a current research hot-spot.Few-shot learning is the algorithm to learn and solve the problem under the condition of limited supervision information.Firstly,it describes the reasons why few-shot learning is difficult to generalize from the perspective of machine learning theory.Secondly,according to the design motivation of the few-shot learning algorithm,existing algorithms are classified into three categories:representation learning,data expansion and learning strategy,and their advantages and disadvantages are analyzed.Thirdly,we summarize the commonly used few-shot learning evaluation methods and the performance of existing models in public data sets.Finally,we discuss the difficulties and future research trends of small sample image classification technology to provide re-ferences for future research.

Key words: Data expansion, Few-shot learning, Image classification, Learning representation, Transfer learning

CLC Number: 

  • TP181
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