方法对比
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| 领域自适应多层感知器× | 微调多层感知机× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2006–2016 | 1986 (MLP); fine-tuning practice formalised c. 2014 |
| 提出者≠ | Ben-David et al.; Ganin et al. | Rumelhart, Hinton & Williams (MLP); Yosinski et al. (fine-tuning analysis) |
| 类型≠ | Domain adaptation of feedforward neural network | Supervised deep learning with pre-trained weight initialisation |
| 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 | fine-tuned MLP, adapted MLP, domain-adapted multilayer perceptron, MLP fine-tuning |
| 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 Fine-Tuned Multilayer Perceptron starts from weights learned on a source task — or a large general-purpose dataset — and continues training on a smaller target dataset with a reduced learning rate. This reuse of pre-learned representations allows the MLP to converge faster and generalise better than training from scratch, especially when labelled target data is scarce. |
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