Machine learningMachine learning
在线少样本学习
在线少样本学习结合了在线学习的流式更新原理和少样本学习的数据效率目标，使模型能够在数据顺序到达时，仅从少量标记示例中持续适应新任务或新类别——而无需访问完整的历史数据集。
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来源
- Finn, C., Rajeswaran, A., Kakade, S., & Levine, S. (2019). Online Meta-Learning. Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97, 1920–1930. link ↗
- Javed, K., & White, M. (2019). Meta-Learning Representations for Continual Learning. Advances in Neural Information Processing Systems (NeurIPS), 32. link ↗
如何引用本页
ScholarGate. (2026, June 3). Online Few-shot Learning (Streaming Meta-Learning from Scarce Labels). ScholarGate. https://scholargate.app/zh/machine-learning/online-few-shot-learning
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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