Comparer des méthodes

	Réseau antagoniste génératif	Transfert de style neuronal
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2014	2015
Auteur d'origine	Goodfellow, I. et al.	Gatys, L. A.; Ecker, A. S.; Bethge, M.
Type	Generative deep learning (adversarial two-network game)	Iterative optimization over CNN feature statistics
Source fondatrice	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image Style Transfer Using Convolutional Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2414–2423.
Alias	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	NST, artistic style transfer, neural artistic style, CNN style transfer
Apparentées	4	3
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.	Neural Style Transfer (NST) is a deep-learning image synthesis technique, introduced by Gatys, Ecker, and Bethge in 2015, that separates the semantic content of one image from the visual texture and artistic style of another, then recombines them into a single synthesized image by iteratively optimizing pixel values to minimize a combined content and style loss computed from the feature maps of a pretrained convolutional neural network.