Confronta i metodi

	Variational Autoencoder	Modello di diffusione	Rete Generativa Avversaria
Campo	Apprendimento profondo	Apprendimento profondo	Apprendimento profondo
Famiglia	Machine learning	Machine learning	Machine learning
Anno di origine	2014	2020	2014
Ideatore	Kingma, D. P. & Welling, M.	Ho, J., Jain, A. & Abbeel, P.	Goodfellow, I. et al.
Tipo	Deep generative latent-variable model (encoder–decoder)	Generative deep learning (denoising diffusion)	Generative deep learning (adversarial two-network game)
Fonte seminale	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).	Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
Alias	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model	Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Correlati	5	4	4
Sintesi	The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning.	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.