Bandingkan kaedah

	Model Difusi yang Diawasi Secara Lemah	Rangkaian Generatif Adversarial
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2022–2024	2014
Pengasas	Ho et al. (DDPM foundation); weak supervision integration by multiple groups, 2022–2024	Goodfellow, I. et al.
Jenis	Generative model with imperfect supervision	Generative deep learning (adversarial two-network game)
Sumber perintis	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.
Alias	WS-Diffusion, weakly supervised DDPM, label-efficient diffusion model, noisy-label diffusion training	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Berkaitan	6	4
Ringkasan	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.