Salīdzināt metodes

	Vāji uzraudzīta semantiskā segmentācija	Daudzpusīgā apguve
Nozare	Dziļā mācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2014–2016	1970s–2006 (formalized)
Autors	Multiple contributors; Class Activation Mapping (Zhou et al., 2016) is foundational	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Tips	Pixel-level classification with image-level or coarse supervision	Learning paradigm
Pirmavots	Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning Deep Features for Discriminative Localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921–2929.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Citi nosaukumi	WSSS, weak-label segmentation, image-level supervised segmentation, weakly-labeled pixel classification	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Saistītās	4	5
Kopsavilkums	Weakly Supervised Semantic Segmentation (WSSS) trains pixel-level scene parsers using only cheap, coarse annotations — typically image-level class tags — instead of costly dense pixel masks. By generating proxy pseudo-labels from a classification network (via Class Activation Maps or similar localisation cues) and iteratively refining them, WSSS brings full-supervision accuracy within reach at a fraction of the annotation cost.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.