So sánh phương pháp

	Mô hình khuếch tán giám sát yếu	Generative Adversarial Network
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2022–2024	2014
Người khởi xướng	Ho et al. (DDPM foundation); weak supervision integration by multiple groups, 2022–2024	Goodfellow, I. et al.
Loại	Generative model with imperfect supervision	Generative deep learning (adversarial two-network game)
Công trình gốc	Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
Tên gọi khác	WS-Diffusion, weakly supervised DDPM, label-efficient diffusion model, noisy-label diffusion training	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
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 Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation.