Comparar métodos

	Modelo de Difusión Débilmente Supervisado	Modelo de difusión
Campo	Aprendizaje profundo	Aprendizaje profundo
Familia	Machine learning	Machine learning
Año de origen	2022–2024	2020
Autor original	Ho et al. (DDPM foundation); weak supervision integration by multiple groups, 2022–2024	Ho, J., Jain, A. & Abbeel, P.
Tipo	Generative model with imperfect supervision	Generative deep learning (denoising diffusion)
Fuente seminal	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.
Alias	WS-Diffusion, weakly supervised DDPM, label-efficient diffusion model, noisy-label diffusion training	Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM
Relacionados	6	4
Resumen	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.