方法对比
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| 领域自适应多层感知器× | 域自适应卷积神经网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2006–2016 | 2015–2017 |
| 提出者≠ | Ben-David et al.; Ganin et al. | Ganin, Y. & Lempitsky, V. (domain-adversarial framework); Tzeng et al. (ADDA) |
| 类型≠ | Domain adaptation of feedforward neural network | Domain-adaptive deep learning 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., Ustinova, 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-CNN, domain adaptation CNN, domain-adaptive deep convolutional network, CNN with domain adaptation |
| 相关 | 5 | 5 |
| 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 CNN trains a convolutional network on a labeled source domain and adapts its learned feature representations to an unlabeled or lightly labeled target domain, bridging the distribution gap so that visual classifiers transfer reliably across datasets, sensors, or imaging conditions without full re-annotation. |
| ScholarGate数据集 ↗ |
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