方法对比
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| LSTM 迁移学习× | 长短期记忆网络（LSTM）× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2018 (ULMFiT; concept since ~2010) | 1997 |
| 提出者≠ | Howard, J. & Ruder, S. (ULMFiT); general concept: Pan & Yang (2010) | Hochreiter, S. & Schmidhuber, J. |
| 类型≠ | Transfer learning / Sequential model | Recurrent neural network with gated memory cells |
| 开创性文献≠ | 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 ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| 别名 | LSTM Transfer Learning, Pre-trained LSTM, LSTM Fine-Tuning, ULMFiT-style LSTM Transfer | LSTM, LSTM network, LSTM-RNN, long short-term memory RNN |
| 相关≠ | 5 | 4 |
| 摘要≠ | 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. | Long Short-Term Memory (LSTM) is a gated recurrent neural network architecture introduced by Hochreiter and Schmidhuber in 1997. It was designed to learn dependencies across long sequences by using dedicated memory cells and three learned gates — forget, input, and output — that control what information is retained, updated, or passed forward at each time step. |
| ScholarGate数据集 ↗ |
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