Sammenlign metoder

	Svakt veiledet diffusjonsmodell	Selv-supervisert diffusjonsmodell
Fagfelt	Dyp læring	Dyp læring
Familie	Machine learning	Machine learning
Opprinnelsesår	2022–2024	2020–2022
Opphavsperson	Ho et al. (DDPM foundation); weak supervision integration by multiple groups, 2022–2024	Ho, J. et al.; extended by Chen, T. et al. and subsequent self-supervised diffusion works
Type	Generative model with imperfect supervision	Generative model with self-supervised representation objective
Opprinnelig kilde	Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.	Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.
Alias	WS-Diffusion, weakly supervised DDPM, label-efficient diffusion model, noisy-label diffusion training	SSDM, self-supervised score-based model, diffusion-based self-supervised learning, denoising diffusion with self-supervised pretraining
Relaterte	6	2
Sammendrag	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.	A self-supervised diffusion model couples the iterative noise-and-denoise generative process of denoising diffusion probabilistic models with a self-supervised representation learning objective — such as contrastive or masked prediction loss — so that the model simultaneously learns to generate realistic data and to produce semantically meaningful representations without any labeled examples.