survey

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

Authors:

Subrota K. Mondal,

Jyoti Prakash SahooAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 13s

Article No.: 271, Pages 1 - 40

https://doi.org/10.1145/3582688

Published: 13 July 2023 Publication History

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 learning, and meta-learning. Then, we inventively extract prior knowledge related to few-shot learning in the form of a pyramid, which summarizes and classifies previous work in detail from the perspective of challenges. Furthermore, to enrich this survey, we present in-depth analysis and insightful discussions of recent advances in each subsection. What is more, taking computer vision as an example, we highlight the important application of FSL, covering various research hotspots. Finally, we conclude the survey with unique insights into technology trends and potential future research opportunities to guide FSL follow-up research.

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Index Terms

A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Learning paradigms

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 13s

December 2023

1367 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3606252

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 13 July 2023

Online AM: 04 February 2023

Accepted: 23 January 2023

Revised: 16 January 2023

Received: 08 February 2022

Published in CSUR Volume 55, Issue 13s

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https://doi.org/10.1016/j.patcog.2024.111042
Zhou CYu ZYuan XZhou SBu JWang H(2025)Less is more: A closer look at semantic-based few-shot learningInformation Fusion10.1016/j.inffus.2024.102672114(102672)Online publication date: Feb-2025
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