পদ্ধতির তুলনা করুন

	দুর্বলভাবে তত্ত্বাবধানে থাকা ডিফিউশন মডেল	Weakly Supervised Semantic Segmentation
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2022–2024	2014–2016
প্রবর্তক	Ho et al. (DDPM foundation); weak supervision integration by multiple groups, 2022–2024	Multiple contributors; Class Activation Mapping (Zhou et al., 2016) is foundational
ধরন	Generative model with imperfect supervision	Pixel-level classification with image-level or coarse supervision
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	6	4
সারসংক্ষেপ	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.