方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 循环神经网络迁移学习× | LSTM 迁移学习× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010 (TL survey); RNN: 1986 | 2018 (ULMFiT; concept since ~2010) |
| 提出者≠ | Pan, S. J. & Yang, Q. (transfer learning survey); RNN origins: Rumelhart, D. E. et al. (1986) | Howard, J. & Ruder, S. (ULMFiT); general concept: Pan & Yang (2010) |
| 类型≠ | Transfer learning on sequence model | Transfer learning / Sequential model |
| 开创性文献≠ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ | 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 ↗ |
| 别名 | TL-RNN, Pretrained RNN, RNN Transfer Learning, Recurrent Transfer Learning | LSTM Transfer Learning, Pre-trained LSTM, LSTM Fine-Tuning, ULMFiT-style LSTM Transfer |
| 相关 | 5 | 5 |
| 摘要≠ | Transfer Learning with Recurrent Neural Network (TL-RNN) reuses weights learned by an RNN on a large source task — such as language modelling or sequence prediction — and adapts them to a new, often smaller target task. This strategy lets practitioners obtain strong sequence-modelling performance without the need for massive labelled datasets. | Transfer Learning with LSTM is a technique in which a Long Short-Term Memory network is first pre-trained on a large source corpus or task, and then its learned weights are transferred and fine-tuned on a smaller target task. This approach, popularized by ULMFiT (Howard & Ruder, 2018), allows LSTM-based models to reach strong performance even when labeled target data is scarce. |
| ScholarGate数据集 ↗ |
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