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LSTM 迁移学习

LSTM 迁移学习是一种技术，其中长短期记忆（Long Short-Term Memory, LSTM）网络首先在一个大型源语料库或任务上进行预训练，然后将其学习到的权重迁移并微调到一个较小的目标任务上。这种方法由 ULMFiT (Howard & Ruder, 2018) 推广，即使标记的目标数据稀少，也能使基于 LSTM 的模型达到强大的性能。

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LSTM 迁移学习
[需翻译标题：BERT-based Classi…微调长短期记忆网络 (Fine-Tuned LS…门控循环单元 (GRU)长短期记忆网络（LSTM）循环神经网络迁移学习

来源

  1. Howard, J. & Ruder, S. (2018). Universal Language Model Fine-Tuning for Text Classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), 328–339. DOI: 10.18653/v1/P18-1031 ↗
  2. Transfer learning. Wikipedia. link ↗

如何引用本页

ScholarGate. (2026, June 3). Transfer Learning with Long Short-Term Memory Networks. ScholarGate. https://scholargate.app/zh/deep-learning/transfer-learning-with-lstm

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微调长短期记忆网络 (Fine-Tuned LSTM)循环神经网络迁移学习

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ScholarGate — Transfer Learning with LSTM (Transfer Learning with Long Short-Term Memory Networks). 于 2026-06-15 检索自 https://scholargate.app/zh/deep-learning/transfer-learning-with-lstm · 数据集： https://doi.org/10.5281/zenodo.20539026