Machine learningDeep learning / NLP / CV
半监督图像分类
半监督图像分类使用少量标记图像和大量未标记图像来训练深度神经网络。伪标签、一致性正则化和置信度阈值等技术使模型能够利用未标记数据的结构，极大地减少了昂贵的手动标注需求，同时接近全监督的准确性。
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来源
- Lee, D.-H. (2013). Pseudo-Label: The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks. ICML 2013 Workshop on Challenges in Representation Learning. link ↗
- Sohn, K., Berthelot, D., Li, C.-L., Zhang, Z., Carlini, N., Cubuk, E. D., Kurakin, A., Zhang, H., & Raffel, C. (2020). FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence. Advances in Neural Information Processing Systems, 33, 596–608. link ↗
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised Image Classification with Deep Neural Networks. ScholarGate. https://scholargate.app/zh/deep-learning/semi-supervised-image-classification
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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