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	Variational Autoencoder	Diffusionsmodell	Generativt antagonistiskt nätverk
Ämnesområde	Djupinlärning	Djupinlärning	Djupinlärning
Familj	Machine learning	Machine learning	Machine learning
Ursprungsår	2014	2020	2014
Upphovsperson	Kingma, D. P. & Welling, M.	Ho, J., Jain, A. & Abbeel, P.	Goodfellow, I. et al.
Typ	Deep generative latent-variable model (encoder–decoder)	Generative deep learning (denoising diffusion)	Generative deep learning (adversarial two-network game)
Ursprungskälla	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
Närliggande	5	4	4
Sammanfattning	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.