Võrdle meetodeid

	Nõrgalt juhendatud difusioonimudel	Nõrgalt juhendatud semantiline segmenteerimine
Valdkond	Süvaõpe	Süvaõpe
Perekond	Machine learning	Machine learning
Tekkeaasta	2022–2024	2014–2016
Looja	Ho et al. (DDPM foundation); weak supervision integration by multiple groups, 2022–2024	Multiple contributors; Class Activation Mapping (Zhou et al., 2016) is foundational
Tüüp	Generative model with imperfect supervision	Pixel-level classification with image-level or coarse supervision
Algallikas	Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.	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.
Rööpnimetused	WS-Diffusion, weakly supervised DDPM, label-efficient diffusion model, noisy-label diffusion training	WSSS, weak-label segmentation, image-level supervised segmentation, weakly-labeled pixel classification
Seotud	6	4
Kokkuvõte	A weakly supervised diffusion model trains or conditions a denoising diffusion probabilistic model using coarse, noisy, or incomplete supervision signals — such as image-level class labels, bounding boxes, or crowd-sourced annotations — instead of pixel-precise ground truth. This allows high-quality generative and discriminative outputs in annotation-scarce settings where full labeling is infeasible or prohibitively expensive.	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.