A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities
By Yisheng Song, Ting-Yuan Wang, Puyu Cai, Subrota Kumar Mondal, Jyoti Prakash Sahoo. ACM Computing Surveys, 2022
710 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Epidemiology
View Paper: https://www.semanticscholar.org/paper/74487ea7c7a27a1a45c5a4dc306aea47cef0238d
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In Artificial Intelligence
In Computer Vision and Pattern Recognition
Citations in the corpus
- A Survey on Few-Shot Class-Incremental Learning (2023, background): “Numerous surveys have been conducted on the topic of FSL, proposing various classification approaches (Jadon, 2020; Song et al., 2023a; Wang et al., 2020b).”