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× | 微调循环神经网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010s | 2015–2018 |
| 提出者≠ | Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs) | Popularised by Howard & Ruder (ULMFiT, 2018); RNN fine-tuning concept developed iteratively in the NLP community from ~2015 |
| 类型≠ | Domain-adaptive sequential model | Transfer learning / sequential model adaptation |
| 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 ↗ | Howard, J. & Ruder, S. (2018). Universal Language Model Fine-Tuning for Text Classification. Proceedings of ACL 2018, 328–339. DOI ↗ |
| 别名 | DA-RNN, domain-adaptive RNN, domain-adapted recurrent network, cross-domain RNN | Fine-Tuned RNN, RNN Fine-Tuning, domain-adapted RNN, pre-trained RNN with downstream adaptation |
| 相关 | 6 | 6 |
| 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. | A Fine-Tuned Recurrent Neural Network (RNN) starts from a model pre-trained on large corpora or time-series data and adapts its weights to a specific downstream task through controlled gradient updates. The approach dramatically cuts the labeled data needed for strong sequence modeling performance in text classification, named entity recognition, sentiment analysis, and related tasks. |
| ScholarGate数据集 ↗ |
|
|