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弱监督循环神经网络

弱监督循环神经网络（RNN）在标签来自不完美来源（例如启发式规则、远程监督、众包或生成式标签模型）而非昂贵专家标注的序列上进行训练。这使得研究人员能够在序列任务（如文本分类、命名实体识别或时间序列预测）中利用大规模无标签语料库，前提是完全标注的数据稀缺或成本高昂。

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弱监督循环神经网络

门控循环单元 (GRU)长短期记忆网络（LSTM）循环神经网络弱监督 LSTM 弱监督 Transformer

来源

Ratner, A., De Sa, C., Wu, S., Selsam, D., & Re, C. (2016). Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems (NeurIPS), 29. link ↗
Zhou, Z.-H. (2018). A brief introduction to weakly supervised learning. National Science Review, 5(1), 44–53. DOI: 10.1093/nsr/nwx106 ↗

如何引用本页

ScholarGate. (2026, June 3). Weakly Supervised Recurrent Neural Network. ScholarGate. https://scholargate.app/zh/deep-learning/weakly-supervised-recurrent-neural-network

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弱监督 LSTM

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