Compara mètodes

	Aprenentatge en línia amb pocs exemples	Aprenentatge amb pocs exemples
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2019	2011–2017
Autor original	Finn, C. et al. (online meta-learning formalization)	Lake, B. M.; Vinyals, O.; Finn, C. et al.
Tipus	Online learning + meta-learning hybrid	Meta-learning / low-data learning paradigm
Font seminal	Finn, C., Rajeswaran, A., Kakade, S., & Levine, S. (2019). Online Meta-Learning. Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97, 1920–1930.	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.
Àlies	online meta-learning, streaming few-shot learning, continual few-shot learning, incremental few-shot learning	FSL, low-shot learning, k-shot learning, meta-learning for few examples
Relacionats	4	4
Resum	Online Few-shot Learning combines the streaming update principle of online learning with the data-efficiency goal of few-shot learning, enabling a model to continuously adapt to new tasks or classes from only a handful of labeled examples as data arrives sequentially — without access to the full historical dataset.	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.