Compară metode

	Învățare online cu puține exemple	Învățare cu puține exemple
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2019	2011–2017
Autorul original	Finn, C. et al. (online meta-learning formalization)	Lake, B. M.; Vinyals, O.; Finn, C. et al.
Tip	Online learning + meta-learning hybrid	Meta-learning / low-data learning paradigm
Sursa seminală	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.
Denumiri alternative	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
Înrudite	4	4
Rezumat	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.