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| Nõrgalt juhendatud konvolutsiooniline närvivõrk× | Pildiklassifikatsioon× | |
|---|---|---|
| Valdkond | Süvaõpe | Süvaõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2015–2016 | 2012 (deep CNN era); conceptual roots 1989 (LeCun) |
| Looja≠ | Oquab, M. et al.; Zhou, B. et al. | Krizhevsky, A.; Sutskever, I.; Hinton, G. E. |
| Tüüp≠ | Weakly supervised deep learning | Supervised classification task |
| Algallikas≠ | 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 ↗ | Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105. link ↗ |
| Rööpnimetused | WS-CNN, weakly supervised CNN, CNN with weak labels, CNN with noisy labels | visual classification, image recognition, CNN-based classification, visual categorization |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | 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. | Image classification is the task of assigning a single semantic label to an entire image from a fixed set of categories. Modern approaches rely on deep convolutional neural networks (CNNs) or Vision Transformers (ViTs) trained end-to-end on large labeled datasets such as ImageNet, achieving superhuman accuracy on many benchmarks and underpinning applications from medical imaging to autonomous vehicles. |
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