Porównaj metody

	Słabo nadzorowana grafowa sieć neuronowa	Słabo nadzorowana konwolucyjna sieć neuronowa
Dziedzina	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning
Rok powstania	2017–2019	2015–2016
Twórca	Derived from GNN literature (Scarselli et al. 2009; Kipf & Welling 2017) combined with weak supervision paradigm	Oquab, M. et al.; Zhou, B. et al.
Typ	Graph-based deep learning with imperfect supervision	Weakly supervised deep learning
Źródło pierwotne	Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR 2017).	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.
Inne nazwy	WS-GNN, graph neural network with weak supervision, noisy-label GNN, partially supervised GNN	WS-CNN, weakly supervised CNN, CNN with weak labels, CNN with noisy labels
Pokrewne	6	5
Podsumowanie	A Weakly Supervised Graph Neural Network (WS-GNN) is a graph deep-learning approach that learns from graph-structured data — nodes, edges, and their attributes — when only noisy, partial, or indirectly obtained labels are available. By coupling GNN message passing with noise-robust training strategies, it extends graph learning to real-world settings where clean, fully annotated graphs are scarce or expensive to obtain.	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.