Porovnat metody

	Konvoluční neuronová síť se slabým dohledem	Konvoluční neuronová síť se samoučením
Obor	Hluboké učení	Hluboké učení
Rodina	Machine learning	Machine learning
Rok vzniku	2015–2016	2018–2020
Tvůrce	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
Původní 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.
Další názvy	WS-CNN, weakly supervised CNN, CNN with weak labels, CNN with noisy labels	Self-supervised CNN, SSL-CNN, contrastive CNN, pretext-task CNN
Příbuzné	5	5
Shrnutí	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.