Methoden vergleichen

	Schwaches Graph-Neuronales Netz (Weakly Supervised Graph Neural Network, WS-GNN)	Schwache CNNs (Weakly Supervised Convolutional Neural Network)
Fachgebiet	Deep Learning	Deep Learning
Familie	Machine learning	Machine learning
Entstehungsjahr	2017–2019	2015–2016
Urheber	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
Wegweisende Quelle	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.
Aliasnamen	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
Verwandt	6	5
Zusammenfassung	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.