Methoden vergleichen

	Schwache CNNs (Weakly Supervised Convolutional Neural Network)	Bildklassifizierung
Fachgebiet	Deep Learning	Deep Learning
Familie	Machine learning	Machine learning
Entstehungsjahr	2015–2016	2012 (deep CNN era); conceptual roots 1989 (LeCun)
Urheber	Oquab, M. et al.; Zhou, B. et al.	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.
Typ	Weakly supervised deep learning	Supervised classification task
Wegweisende Quelle	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.	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.
Aliasnamen	WS-CNN, weakly supervised CNN, CNN with weak labels, CNN with noisy labels	visual classification, image recognition, CNN-based classification, visual categorization
Verwandt	5	5
Zusammenfassung	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.