Salīdzināt metodes

	Vāji uzraudzīta semantiskā segmentācija	Pašuzraudzības apmācība
Nozare	Dziļā mācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2014–2016	2018–2020
Autors	Multiple contributors; Class Activation Mapping (Zhou et al., 2016) is foundational	LeCun, Y. and community (formalized ~2018–2020)
Tips	Pixel-level classification with image-level or coarse supervision	Representation 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.	LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/
Citi nosaukumi	WSSS, weak-label segmentation, image-level supervised segmentation, weakly-labeled pixel classification	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Saistītās	4	3
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.	Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples.