方法对比
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| 半监督目标检测× | 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≠ | 2020–2021 | 2013–2020 |
| 提出者≠ | Sohn et al. (STAC); Liu et al. (Unbiased Teacher) | Lee, D.-H. (pseudo-label); Sohn et al. (FixMatch) |
| 类型≠ | Semi-supervised learning for detection | Semi-supervised deep learning |
| 开创性文献≠ | Sohn, K., Zhang, Z., Li, C.-L., Zhang, H., Lee, C.-Y., & Pfister, T. (2020). A Simple Semi-Supervised Learning Framework for Object Detection. arXiv preprint arXiv:2005.04757. 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 | SSOD, semi-supervised detection, pseudo-label object detection, label-efficient object detection | SSL image classification, semi-supervised CNN classification, pseudo-label image classification, label-efficient image classification |
| 相关≠ | 6 | 5 |
| 摘要≠ | Semi-supervised object detection trains a detector on a small labeled image set and a large unlabeled image set. A teacher model generates pseudo-labels for unlabeled images, and a student model learns from both real and pseudo-labeled data, dramatically reducing the expensive manual bounding-box annotation burden while achieving accuracy competitive with fully supervised baselines. | 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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