Machine learningDeep learning / NLP / CV
域自适应卷积神经网络
域自适应卷积神经网络（CNN）在带标签的源域上训练卷积网络，并使其学习到的特征表示适应无标签或少量标签的目标域，从而弥合分布差距，使视觉分类器能够在数据集、传感器或成像条件之间可靠地迁移，而无需完全重新标注。
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来源
- 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 ↗
- Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017). Adversarial discriminative domain adaptation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7167–7176. DOI: 10.1109/CVPR.2017.316 ↗
如何引用本页
ScholarGate. (2026, June 3). Domain-adaptive Convolutional Neural Network (DA-CNN). ScholarGate. https://scholargate.app/zh/deep-learning/domain-adaptive-convolutional-neural-network
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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