Comparer des méthodes

	Autoencodeur variationnel semi-supervisé	Réseau antagoniste génératif
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2014	2014
Auteur d'origine	Kingma, D. P.; Mohamed, S.; Rezende, D. J.; Wierstra, D.	Goodfellow, I. et al.
Type	Generative probabilistic model (semi-supervised)	Generative deep learning (adversarial two-network game)
Source fondatrice	Kingma, D. P., Mohamed, S., Rezende, D. J., & Wierstra, D. (2014). Semi-supervised learning with deep generative models. Advances in Neural Information Processing Systems (NeurIPS), 27, 3581–3589.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
Alias	Semi-supervised VAE, M2 model, VAE with label propagation, deep generative semi-supervised model	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Apparentées	6	4
Résumé	The semi-supervised VAE (M2 model) is a deep generative method that jointly learns a latent representation of inputs and a classifier, leveraging both labeled and unlabeled examples in a principled probabilistic framework. Introduced by Kingma et al. in 2014, it allows accurate classification even when labels are scarce by having the generative model explain away unlabeled observations.	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.