A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities
East China Normal University · Michigan State University · +2 more institutions
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
- FWCI
- 98.36
- Percentile
- 100%
- References
- 258
Authors
5Topics & keywords
- Computer science
- Data science
- Context (archaeology)
- Perspective (graphical)
- Confusion
- Strengths and weaknesses
- Artificial intelligence
- Epistemology