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。
| 域自适应循环神经网络× | 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≠ | 2010s | 1997 |
| 提出者≠ | Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs) | Hochreiter, S. & Schmidhuber, J. |
| 类型≠ | Domain-adaptive sequential model | Recurrent neural network with gated memory cells |
| 开创性文献≠ | Ganin, Y., Ustunova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35. link ↗ | Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. DOI ↗ |
| 别名 | DA-RNN, domain-adaptive RNN, domain-adapted recurrent network, cross-domain RNN | LSTM, LSTM network, LSTM-RNN, long short-term memory RNN |
| 相关≠ | 6 | 4 |
| 摘要≠ | A Domain-adaptive Recurrent Neural Network (DA-RNN) is a recurrent neural network trained on a source domain and adapted to a target domain using domain adaptation techniques such as adversarial training, feature alignment, or fine-tuning. It enables sequential models to generalise across domains when labeled target-domain data is scarce or unavailable. | 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 ↗ |
|
|