เปรียบเทียบวิธี

	Weakly supervised convolutional neural network	Semantic Segmentation
สาขาวิชา	การเรียนรู้เชิงลึก	การเรียนรู้เชิงลึก
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2015–2016	2015
ผู้ริเริ่ม	Oquab, M. et al.; Zhou, B. et al.	Long, J., Shelhamer, E., & Darrell, T.
ประเภท	Weakly supervised deep learning	Dense prediction / pixel-wise classification
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	WS-CNN, weakly supervised CNN, CNN with weak labels, CNN with noisy labels	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation
ที่เกี่ยวข้อง	5	5
สรุป	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.