পদ্ধতির তুলনা করুন

	ফিউ-শট লার্নিং	মেট্রিক লার্নিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2011–2017	2003 (foundational); refined 2009 (LMNN)
প্রবর্তক	Lake, B. M.; Vinyals, O.; Finn, C. et al.	Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.
ধরন	Meta-learning / low-data learning paradigm	Representation learning / supervised distance optimization
মৌলিক উৎস	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.	Xing, E. P., Jordan, M. I., Russell, S., & Ng, A. Y. (2003). Distance metric learning with application to clustering with side-information. In Advances in Neural Information Processing Systems (NIPS), 16, 505–512.
অপর নাম	FSL, low-shot learning, k-shot learning, meta-learning for few examples	Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance
সম্পর্কিত	4	5
সারসংক্ষেপ	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.	Metric learning is a machine-learning framework that trains a distance or similarity function from data so that semantically similar examples end up close together in the learned space while dissimilar examples are pushed apart. Unlike fixed distances such as Euclidean, the learned metric adapts to the structure of the task, making downstream classifiers, clusterers, and retrieval systems significantly more accurate.