विधियों की तुलना करें

	फ्यू-शॉट लर्निंग	अर्ध-पर्यवेक्षित शिक्षण
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2011–2017	1970s–2006 (formalized)
प्रवर्तक	Lake, B. M.; Vinyals, O.; Finn, C. et al.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
प्रकार	Meta-learning / low-data learning paradigm	Learning paradigm
मौलिक स्रोत	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.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
उपनाम	FSL, low-shot learning, k-shot learning, meta-learning for few examples	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
संबंधित	4	5
सारांश	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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.