قارن الطرق

	تعلم العينات القليلة عبر الإنترنت	تعلم العينات القليلة
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2019	2011–2017
صاحب الطريقة	Finn, C. et al. (online meta-learning formalization)	Lake, B. M.; Vinyals, O.; Finn, C. et al.
النوع	Online learning + meta-learning hybrid	Meta-learning / low-data learning paradigm
المصدر التأسيسي	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.
الأسماء البديلة	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
ذات صلة	4	4
الملخص	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.