手法を比較

	Generative Adversarial Network	ニューラルスタイル変換
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2014	2015
提唱者	Goodfellow, I. et al.	Gatys, L. A.; Ecker, A. S.; Bethge, M.
種類	Generative deep learning (adversarial two-network game)	Iterative optimization over CNN feature statistics
原典	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.
別名	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	NST, artistic style transfer, neural artistic style, CNN style transfer
関連	4	3
概要	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.