Machine learningDeep learning / NLP / CV
半监督卷积神经网络
半监督CNN同时在少量标记图像集和大量未标记图像池上训练卷积网络，利用伪标记和一致性正则化等技术从无标记数据中提取监督信号。这种策略在无需额外人工标记工作的情况下，大大缩小了因标注稀缺而导致的性能差距。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
+2 more
来源
- Lee, D.-H. (2013). Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. ICML Workshop on Challenges in Representation Learning. link ↗
- Tarvainen, A. & Valpola, H. (2017). Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in Neural Information Processing Systems (NeurIPS), 30. link ↗
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised Convolutional Neural Network (SSL-CNN). ScholarGate. https://scholargate.app/zh/deep-learning/semi-supervised-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.
- 微调卷积神经网络深度学习↔ compare
- 自监督卷积神经网络深度学习↔ compare
- 半监督图像分类深度学习↔ compare
- 基于卷积神经网络的迁移学习深度学习↔ compare
- 弱监督卷积神经网络深度学习↔ compare