Machine learningMachine learning
贝叶斯少样本学习
贝叶斯少样本学习将贝叶斯推理与元学习相结合，使模型能够从每个类别的少量一到五个标记示例中进行泛化。通过将特定任务的参数视为随机变量，并在多个训练任务中学习信息性先验，该方法除了预测外还能产生校准的不确定性估计——这是确定性少样本学习器的关键优势。
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来源
- Gordon, J., Bronskill, J., Bauer, M., Nowozin, S. & Turner, R. E. (2019). Meta-Learning Probabilistic Inference for Prediction. International Conference on Learning Representations (ICLR 2019). link ↗
- Finn, C., Xu, K. & Levine, S. (2018). Probabilistic Model-Agnostic Meta-Learning. Advances in Neural Information Processing Systems (NeurIPS 2018), 31. link ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Few-Shot Learning (Meta-Learning with Bayesian Inference). ScholarGate. https://scholargate.app/zh/machine-learning/bayesian-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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