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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.

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Sources

  1. 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 ↗
  2. 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 ↗

Related methods

Fine-Tuned Convolutional Neural NetworkImage ClassificationSelf-supervised convolutional neural networkSemantic SegmentationSemi-supervised Convolutional Neural Network

Referenced by

Semi-supervised Convolutional Neural NetworkWeakly supervised graph neural networkWeakly supervised multilayer perceptron

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ScholarGate — Weakly supervised convolutional neural network (Weakly Supervised Convolutional Neural Network). Retrieved 2026-06-04 from https://scholargate.app/tr/deep-learning/weakly-supervised-convolutional-neural-network