方法对比
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| 半监督卷积神经网络× | 半监督图像分类× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2013–2017 | 2013–2020 |
| 提出者≠ | Lee, D.-H.; Tarvainen, A. & Valpola, H. (among others) | Lee, D.-H. (pseudo-label); Sohn et al. (FixMatch) |
| 类型 | Semi-supervised deep learning | Semi-supervised deep learning |
| 开创性文献≠ | 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 ↗ | 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 ↗ |
| 别名 | SSL-CNN, semi-supervised CNN, self-training CNN, pseudo-label CNN | SSL image classification, semi-supervised CNN classification, pseudo-label image classification, label-efficient image classification |
| 相关 | 5 | 5 |
| 摘要≠ | A Semi-supervised CNN trains a convolutional network on a small labeled image set and a larger pool of unlabeled images simultaneously, using techniques such as pseudo-labeling and consistency regularization to extract supervisory signal from unlabeled data. This strategy closes much of the performance gap caused by scarce annotations without requiring additional human labeling effort. | Semi-supervised image classification trains deep neural networks on a small set of labeled images together with a much larger pool of unlabeled images. Techniques such as pseudo-labeling, consistency regularization, and confidence thresholding allow the model to leverage the structure of unlabeled data, dramatically reducing the need for expensive manual annotation while approaching fully-supervised accuracy. |
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