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集成少样本学习

集成少样本学习（Ensemble Few-Shot Learning）结合了多个少样本模型（例如，原型网络或嵌入学习器），以仅从一到少数几个标记示例中对新类别进行分类。通过强制基础学习器之间的多样性并聚合它们的预测，该集成在准确性和鲁棒性方面始终优于任何单一的少样本模型，尤其是在标签极度稀缺的情况下。

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集成少样本学习
Boosting少样本学习半监督少样本学习迁移学习投票集成 (Voting Ensemble)

来源

  1. Dvornik, N., Schmid, C., & Mairal, J. (2019). Diversity with Cooperation: Ensemble Methods for Few-Shot Classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3716–3725. link ↗
  2. Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2020). Generalizing from a Few Examples: A Survey on Few-Shot Learning. ACM Computing Surveys, 53(3), 1–34. DOI: 10.1145/3386252 ↗

如何引用本页

ScholarGate. (2026, June 3). Ensemble Methods for Few-Shot Learning. ScholarGate. https://scholargate.app/zh/machine-learning/ensemble-few-shot-learning

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ScholarGate — Ensemble Few-shot learning (Ensemble Methods for Few-Shot Learning). 于 2026-06-15 检索自 https://scholargate.app/zh/machine-learning/ensemble-few-shot-learning · 数据集： https://doi.org/10.5281/zenodo.20539026