Сравнение методов

	Слабо контролируемая диффузионная модель	Генеративно-состязательная сеть
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2022–2024	2014
Автор метода	Ho et al. (DDPM foundation); weak supervision integration by multiple groups, 2022–2024	Goodfellow, I. et al.
Тип	Generative model with imperfect supervision	Generative deep learning (adversarial two-network game)
Основополагающий источник	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.
Другие названия	WS-Diffusion, weakly supervised DDPM, label-efficient diffusion model, noisy-label diffusion training	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Связанные	6	4
Сводка	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.