Porovnať metódy

	Slabá konvolučná neurónová sieť	Konvolučná neuronová sieť so samoučiacim sa dohľadom
Odbor	Hlboké učenie	Hlboké učenie
Rodina	Machine learning	Machine learning
Rok vzniku	2015–2016	2018–2020
Tvorca	Oquab, M. et al.; Zhou, B. et al.	LeCun, Y. (CNN backbone); Chen et al. and He et al. (self-supervised visual frameworks)
Typ	Weakly supervised deep learning	Self-supervised deep learning
Pôvodný zdroj	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.	Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning (ICML 2020), PMLR 119, 1597–1607.
Ďalšie názvy	WS-CNN, weakly supervised CNN, CNN with weak labels, CNN with noisy labels	Self-supervised CNN, SSL-CNN, contrastive CNN, pretext-task CNN
Príbuzné	5	5
Zhrnutie	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.	A self-supervised convolutional neural network (CNN) learns powerful visual representations from unlabeled images by solving pretext tasks — such as contrastive instance discrimination or masked-patch prediction — and then fine-tunes on a small labeled set. This approach dramatically reduces dependence on large annotated datasets while preserving the spatial feature-extraction strengths of convolutional architectures.