手法を比較

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