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Method evidence record

Few-shot Learning

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.

Evidence statusSources recorded, not reviewedBibliographic sources are present. Claim-level evidence review has not been performed.

Source record

Recorded

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Few-shot Learning (Meta-learning with Limited Labeled Examples)

Taxonomic method recordml-modelmachine-learning

  1. 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. · URL

  2. Finn, C., Abbeel, P., & Levine, S. (2017). Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. Proceedings of the 34th International Conference on Machine Learning (ICML), PMLR 70:1126–1135. · URL

Open full method2 recorded citations, copied from the method source record.

Curated claims

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No curated claims yet

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Related methods

Suggested4

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