方法对比
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| 领域自适应多层感知器× | 多层感知机 (MLP)× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2006–2016 | 1986 |
| 提出者≠ | Ben-David et al.; Ganin et al. | Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. |
| 类型≠ | Domain adaptation of feedforward neural network | Supervised feedforward neural network |
| 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 ↗ | Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536. DOI ↗ |
| 别名≠ | DA-MLP, domain-adaptive MLP, domain-adapted feedforward network, domain adaptation with MLP | MLP, feedforward neural network, fully connected neural network, vanilla neural network |
| 相关≠ | 5 | 4 |
| 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 Multilayer Perceptron is a classic fully connected feedforward neural network trained with the backpropagation algorithm, as formalised by Rumelhart, Hinton & Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons, and an output layer, the MLP learns nonlinear mappings from input features to target outputs and serves as the foundational building block of modern deep learning. |
| ScholarGate数据集 ↗ |
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