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	नियमितीकृत अल्प-शिक्षण (Regularized Few-Shot Learning)	फ्यू-शॉट लर्निंग
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2016-2020	2011–2017
प्रवर्तक	Multiple (Chen et al., Tian et al., Snell et al., and others)	Lake, B. M.; Vinyals, O.; Finn, C. et al.
प्रकार	Meta-learning framework with explicit regularization	Meta-learning / low-data learning paradigm
मौलिक स्रोत	Chen, W., Liu, Y., Kira, Z., Wang, Y. F., & Huang, J. (2019). A Closer Look at Few-Shot Classification. International Conference on Learning Representations (ICLR).	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.
उपनाम	FSL with regularization, regularized meta-learning, few-shot learning with regularization, regularized episodic learning	FSL, low-shot learning, k-shot learning, meta-learning for few examples
संबंधित	5	4
सारांश	Regularized few-shot learning augments standard few-shot learning pipelines with explicit regularization mechanisms — such as weight decay, dropout, data augmentation, label smoothing, or manifold constraints — to reduce overfitting to the tiny support sets that define each episode. This produces more generalizable models when only one to thirty labeled examples per class are available.	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.