方法对比
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| 弱监督卷积神经网络× | 自监督卷积神经网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2015–2016 | 2018–2020 |
| 提出者≠ | Oquab, M. et al.; Zhou, B. et al. | LeCun, Y. (CNN backbone); Chen et al. and He et al. (self-supervised visual frameworks) |
| 类型≠ | Weakly supervised deep learning | Self-supervised deep learning |
| 开创性文献≠ | Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2921–2929. DOI ↗ | Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning (ICML 2020), PMLR 119, 1597–1607. link ↗ |
| 别名 | WS-CNN, weakly supervised CNN, CNN with weak labels, CNN with noisy labels | Self-supervised CNN, SSL-CNN, contrastive CNN, pretext-task CNN |
| 相关 | 5 | 5 |
| 摘要≠ | A weakly supervised CNN is a convolutional neural network trained with incomplete, coarse, or noisy annotations instead of full pixel-level or bounding-box labels. Typical weak labels include image-level class tags, partial annotations, or crowd-sourced noisy labels. The model learns to classify and often to roughly localize objects using these cheaper, lower-quality supervision signals. | A self-supervised convolutional neural network (CNN) learns powerful visual representations from unlabeled images by solving pretext tasks — such as contrastive instance discrimination or masked-patch prediction — and then fine-tunes on a small labeled set. This approach dramatically reduces dependence on large annotated datasets while preserving the spatial feature-extraction strengths of convolutional architectures. |
| ScholarGate数据集 ↗ |
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