Comparer des méthodes

	Réseau antagoniste génératif	Autoencodeur Variationnel	Vision Transformer
Domaine	Apprentissage profond	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	2014	2014	2021
Auteur d'origine	Goodfellow, I. et al.	Kingma, D. P. & Welling, M.	Dosovitskiy, A. et al.
Type	Generative deep learning (adversarial two-network game)	Deep generative latent-variable model (encoder–decoder)	Transformer architecture for images (self-attention over patches)
Source fondatrice	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR).	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Alias	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Apparentées	4	5	5
Résumé	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 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.	The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs).