विधियों की तुलना करें

	कमजोर पर्यवेक्षित प्रसार मॉडल	जनरेटिव एडवरसैरियल नेटवर्क
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2022–2024	2014
प्रवर्तक	Ho et al. (DDPM foundation); weak supervision integration by multiple groups, 2022–2024	Goodfellow, I. et al.
प्रकार	Generative model with imperfect supervision	Generative deep learning (adversarial two-network game)
मौलिक स्रोत	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.
उपनाम	WS-Diffusion, weakly supervised DDPM, label-efficient diffusion model, noisy-label diffusion training	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
संबंधित	6	4
सारांश	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.