Compară metode

	Clasificare de Imagini cu Supervizare Slabă	Clasificarea Imaginilor
Domeniu	Învățare profundă	Învățare profundă
Familie	Machine learning	Machine learning
Anul apariției	2014–2016	2012 (deep CNN era); conceptual roots 1989 (LeCun)
Autorul original	Multiple contributors; class activation map approach: Zhou et al.	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.
Tip	Weakly supervised deep learning paradigm	Supervised classification task
Sursa seminală	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.
Denumiri alternative	WSL image classification, image-level supervised classification, noisy-label image classification, weakly labeled visual recognition	visual classification, image recognition, CNN-based classification, visual categorization
Înrudite	5	5
Rezumat	Weakly supervised image classification trains convolutional or transformer-based networks using only coarse, incomplete, or noisy supervision — such as image-level category labels, hashtags, or web-scraped tags — without requiring precise bounding boxes or pixel annotations. This dramatically reduces labeling cost while still enabling high-accuracy visual recognition at scale.	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.