Сравнение на методи

	Дифузионен модел	Генеративна състезателна мрежа	Методът на опорните вектори (класификация)
Област	Дълбоко обучение	Дълбоко обучение	Машинно обучение
Семейство	Machine learning	Machine learning	Machine learning
Година на възникване	2020	2014	1995
Създател	Ho, J., Jain, A. & Abbeel, P.	Goodfellow, I. et al.	Cortes, C. & Vapnik, V.
Тип	Generative deep learning (denoising diffusion)	Generative deep learning (adversarial two-network game)	Maximum-margin classifier (kernel method)
Основополагащ източник	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.
Други названия	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
Свързани	4	4	5
Резюме	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.