方法对比
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| 领域自适应多层感知器× | 域自适应循环神经网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2006–2016 | 2010s |
| 提出者≠ | Ben-David et al.; Ganin et al. | Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs) |
| 类型≠ | Domain adaptation of feedforward neural network | Domain-adaptive sequential model |
| 开创性文献≠ | Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Vaughan, J. W. (2010). A theory of learning from different domains. Machine Learning, 79(1–2), 151–175. DOI ↗ | 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 ↗ |
| 别名 | DA-MLP, domain-adaptive MLP, domain-adapted feedforward network, domain adaptation with MLP | DA-RNN, domain-adaptive RNN, domain-adapted recurrent network, cross-domain RNN |
| 相关≠ | 5 | 6 |
| 摘要≠ | A domain-adaptive multilayer perceptron (DA-MLP) is a feedforward neural network trained to learn representations that are useful across a labeled source domain and an unlabeled or differently distributed target domain. By minimizing both a task loss and a domain-discrepancy objective, the MLP generalizes to the target domain with little or no target-domain labels. | 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. |
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