Σύγκριση μεθόδων

	Ασθενώς Επιβλεπόμενο Συνελικτικό Νευρωνικό Δίκτυο	Σημασιολογική Τμηματοποίηση
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2015–2016	2015
Δημιουργός	Oquab, M. et al.; Zhou, B. et al.	Long, J., Shelhamer, E., & Darrell, T.
Τύπος	Weakly supervised deep learning	Dense prediction / pixel-wise classification
Θεμελιώδης πηγή	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.	Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440.
Εναλλακτικές ονομασίες	WS-CNN, weakly supervised CNN, CNN with weak labels, CNN with noisy labels	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation
Συναφείς	5	5
Σύνοψη	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.	Semantic segmentation assigns a class label to every pixel in an image, producing a dense, category-annotated map of the scene. Unlike object detection, which draws bounding boxes, it delineates the exact spatial extent of each class, making it indispensable in medical imaging, autonomous driving, satellite analysis, and any task where precise region boundaries matter.