Few-shot learning
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Few-shot learning (FSL) is a problem setup in machine learning in which a model learns to perform a task, typically classification, from only a small number of labeled examples per class, rather than the large datasets required by conventional supervised learning.[1][2][3][4]
One-shot learning is the special case of the N-way K-shot framing in which K equals one, such that the model must generalize from exactly one example per class.[1][5][6][7]
The limiting case of few-shot learning is zero-shot learning that requires no training on examples of the class to be classified; instead, the classification task must be generalized from examples of other classes.[6][8][9]
See also
[edit]References
[edit]- 1 2 Wang, Yaqing; Yao, Quanming; Kwok, James T.; Ni, Lionel M. (2020-06-12). "Generalizing from a Few Examples: A Survey on Few-shot Learning". ACM Comput. Surv. 53 (3): 63:1–63:34. doi:10.1145/3386252. ISSN 0360-0300.
- ↑ Song, Yisheng; Wang, Ting; Cai, Puyu; Mondal, Subrota K.; Sahoo, Jyoti Prakash (2023-07-13). "A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities". ACM Comput. Surv. 55 (13s): 271:1–271:40. doi:10.1145/3582688. ISSN 0360-0300.
- ↑ Parnami, Archit; Lee, Minwoo (2022-03-07), Learning from Few Examples: A Summary of Approaches to Few-Shot Learning, arXiv:2203.04291
- ↑ Han, Xiaoming; Shi, Dianxi; Wang, Zhen; Chen, Yang; Jin, Songchang; Yang, Shaowu (2026-01-17). "Meta-learning for few-shot open task recognition". Scientific Reports. 16 (1): 5624. Bibcode:2026NatSR..16.5624H. doi:10.1038/s41598-026-36291-x. ISSN 2045-2322. PMC 12891690. PMID 41547899.
- ↑ Tyukin, Ivan Y.; Gorban, Alexander N.; Alkhudaydi, Muhammad H.; Zhou, Qinghua (2021-07-18). "Demystification of Few-shot and One-shot Learning". 2021 International Joint Conference on Neural Networks (IJCNN). IEEE. pp. 1–7. Bibcode:2021ijcn.conf..800T. doi:10.1109/IJCNN52387.2021.9534395. ISBN 978-1-6654-3900-8.
- 1 2 Kadam, Suvarna; Vaidya, Vinay (2020). "Review and Analysis of Zero, One and Few Shot Learning Approaches". In Abraham, Ajith; Cherukuri, Aswani Kumar; Melin, Patricia; Gandhi, Niketa (eds.). Intelligent Systems Design and Applications. Advances in Intelligent Systems and Computing. Vol. 940. Cham: Springer International Publishing. pp. 100–112. doi:10.1007/978-3-030-16657-1_10. ISBN 978-3-030-16657-1.
- ↑ Vinyals, Oriol; Blundell, Charles; Lillicrap, Timothy; Kavukcuoglu, Koray; Wierstra, Daan (2016-12-05). Matching networks for one shot learning. Red Hook, NY, USA: Curran Associates Inc. pp. 3637–3645. ISBN 978-1-5108-3881-9.
- ↑ Wang, Wei; Zheng, Vincent W.; Yu, Han; Miao, Chunyan (2019-01-16). "A Survey of Zero-Shot Learning: Settings, Methods, and Applications". ACM Trans. Intell. Syst. Technol. 10 (2): 13:1–13:37. doi:10.1145/3293318. ISSN 2157-6904.
- ↑ Xian, Yongqin; Lampert, Christoph H.; Schiele, Bernt; Akata, Zeynep (2019-09-01). "Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly". IEEE Transactions on Pattern Analysis and Machine Intelligence. 41 (9): 2251–2265. Bibcode:2019ITPAM..41.2251X. doi:10.1109/TPAMI.2018.2857768. ISSN 0162-8828. PMID 30028691.
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