Confronta i metodi

	Multilayer Perceptron Debolmente Supervisionato	Rete Neurale Convoluzionale Debolmente Supervisionata
Campo	Apprendimento profondo	Apprendimento profondo
Famiglia	Machine learning	Machine learning
Anno di origine	2016–2018	2015–2016
Ideatore	Multiple contributors; paradigm formalized by Zhou (2018) and Ratner et al. (2016)	Oquab, M. et al.; Zhou, B. et al.
Tipo	Feedforward neural network trained under weak supervision	Weakly supervised deep learning
Fonte seminale	Zhou, Z.-H. (2018). A brief introduction to weakly supervised learning. National Science Review, 5(1), 44–53.	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.
Alias	WS-MLP, weakly supervised feedforward network, noisy-label MLP, weak-label multilayer perceptron	WS-CNN, weakly supervised CNN, CNN with weak labels, CNN with noisy labels
Correlati	5	5
Sintesi	A Weakly Supervised Multilayer Perceptron trains a standard feedforward neural network when only imperfect supervision is available — labels may be noisy, incomplete, crowd-sourced, rule-generated, or derived from distant supervision — enabling learning at scale without the cost of full expert annotation.	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.