So sánh phương pháp

	Mô hình khuếch tán giám sát yếu	Mô hình khuếch tán
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2022–2024	2020
Người khởi xướng	Ho et al. (DDPM foundation); weak supervision integration by multiple groups, 2022–2024	Ho, J., Jain, A. & Abbeel, P.
Loại	Generative model with imperfect supervision	Generative deep learning (denoising diffusion)
Công trình gốc	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. NeurIPS.
Tên gọi khác	WS-Diffusion, weakly supervised DDPM, label-efficient diffusion model, noisy-label diffusion training	Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM
Liên quan	6	4
Tóm tắt	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 diffusion model is a generative deep-learning method, introduced by Ho, Jain and Abbeel in 2020 (DDPM), that learns to produce high-quality images, audio and molecular structures by reversing a step-by-step noising process. It has largely displaced GANs as the current state of the art in generative modelling.