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少样本学习

少样本学习是一种机器学习范式，它通过利用从大规模相关训练分布中获得的先验知识，使模型能够仅凭少数（通常为一到五）标记示例来识别新类别或解决新任务。在标记成本高昂、稀缺或结构受限的领域，它尤其具有相关性。

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少样本学习
度量学习自监督学习半监督学习迁移学习主动学习与自监督学习贝叶斯主动学习贝叶斯少样本学习贝叶斯度量学习贝叶斯半监督学习贝叶斯迁移学习

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来源

  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. link ↗
  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. link ↗

如何引用本页

ScholarGate. (2026, June 3). Few-shot Learning (Meta-learning with Limited Labeled Examples). ScholarGate. https://scholargate.app/zh/machine-learning/few-shot-learning

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被引用于

主动学习与自监督学习贝叶斯主动学习贝叶斯少样本学习贝叶斯度量学习贝叶斯半监督学习贝叶斯迁移学习集成少样本学习集成度量学习集成迁移学习度量学习在线主动学习在线少样本学习在线学习在线迁移学习正则化少样本学习正则化迁移学习鲁棒主动学习鲁棒度量学习Self-supervised Federated Learning自监督学习自监督命名实体识别自监督迁移学习半监督联邦学习半监督少样本学习半监督学习半监督度量学习迁移学习

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ScholarGate — Few-shot Learning (Few-shot Learning (Meta-learning with Limited Labeled Examples)). 于 2026-06-15 检索自 https://scholargate.app/zh/machine-learning/few-shot-learning · 数据集： https://doi.org/10.5281/zenodo.20539026