Compara mètodes

	Model de difusió	Generative Adversarial Network	Màquina de Vectors de Suport (Classificació)
Camp	Aprenentatge profund	Aprenentatge profund	Aprenentatge automàtic
Família	Machine learning	Machine learning	Machine learning
Any d'origen	2020	2014	1995
Autor original	Ho, J., Jain, A. & Abbeel, P.	Goodfellow, I. et al.	Cortes, C. & Vapnik, V.
Tipus	Generative deep learning (denoising diffusion)	Generative deep learning (adversarial two-network game)	Maximum-margin classifier (kernel method)
Font seminal	Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Àlies	Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Relacionats	4	4	5
Resum	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.	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.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.