Machine learningDeep learning / NLP / CV
弱监督卷积神经网络
弱监督卷积神经网络（CNN）是指使用不完整、粗略或有噪声的标注来训练的卷积神经网络，而不是使用完整的像素级或边界框标签。典型的弱标签包括图像级类别标签、部分标注或众包的噪声标签。模型利用这些成本较低、质量较低的监督信号来学习分类，并常常能粗略地定位物体。
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来源
- 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: 10.1109/CVPR.2016.319 ↗
- Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2015). Is object localization for free? — Weakly-supervised learning with convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 685–694. DOI: 10.1109/CVPR.2015.7298668 ↗
如何引用本页
ScholarGate. (2026, June 3). Weakly Supervised Convolutional Neural Network. ScholarGate. https://scholargate.app/zh/deep-learning/weakly-supervised-convolutional-neural-network
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