reviewACM Computing SurveysFeb 4, 2023Closed access

A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities

East China Normal University · Michigan State University · +2 more institutions

Indexed incrossref

Abstract

Few-shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few or even zero samples remains a serious challenge. In this context, we extensively investigated 200+ FSL papers published in top journals and conferences in the past three years, aiming to present a timely and comprehensive overview of the most recent advances in FSL with a fresh perspective and to provide an impartial comparison of the strengths and weaknesses of existing work. To avoid conceptual confusion, we first elaborate and contrast a set of relevant concepts including few-shot learning, transfer…

Citation impact

597
total citations
FWCI
98.36
Percentile
100%
References
258
Citations per year

Authors

5

Topics & keywords

Keywords
  • Computer science
  • Data science
  • Context (archaeology)
  • Perspective (graphical)
  • Confusion
  • Strengths and weaknesses
  • Artificial intelligence
  • Epistemology
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Funding