方法证据记录
Weakly supervised convolutional neural network
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Weakly Supervised Convolutional Neural Network
分类方法记录 · ml-model / deep-learning
- 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
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