Sammenlign metoder

	Few-shot Learning	Overførselslæring
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2011–2017	2010 (formalized); 1990s (early roots)
Ophavsperson	Lake, B. M.; Vinyals, O.; Finn, C. et al.	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Type	Meta-learning / low-data learning paradigm	Learning paradigm
Oprindelig kilde	Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Aliasser	FSL, low-shot learning, k-shot learning, meta-learning for few examples	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Relaterede	4	3
Resumé	Few-shot learning is a machine learning paradigm that trains models to recognize new classes or solve new tasks from only a handful of labeled examples — typically one to five — by leveraging prior knowledge acquired from a large, related training distribution. It is especially relevant in domains where labeling is expensive, scarce, or structurally limited.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.