Confronta i metodi

	Rete Generativa Avversaria	Modello di diffusione
Campo	Apprendimento profondo	Apprendimento profondo
Famiglia	Machine learning	Machine learning
Anno di origine	2014	2020
Ideatore	Goodfellow, I. et al.	Ho, J., Jain, A. & Abbeel, P.
Tipo	Generative deep learning (adversarial two-network game)	Generative deep learning (denoising diffusion)
Fonte seminale	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS.
Alias	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM
Correlati	4	4
Sintesi	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.	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.