Comparer des méthodes

	Autoencodeur	Réseau antagoniste génératif
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2006	2014
Auteur d'origine	Hinton, G.E. & Salakhutdinov, R.R.	Goodfellow, I. et al.
Type	Neural network (encoder-decoder)	Generative deep learning (adversarial two-network game)
Source fondatrice	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
Alias	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Apparentées	4	4
Résumé	An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data.	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.