ZotWeb	paper-conference
Src Url	Gatys, Ecker, Bethge (2016)

Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from style. Here we use image representations derived from Convolutional Neural Networks optimised for object recognition, which make high level image information explicit. We introduce A Neural Algorithm of Artistic Style that can separate and recombine the image content and style of natural images. The algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous wellknown artworks. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation.

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Image Style Transfer Using Convolutional Neural Networks

Citer: (Gatys et al., 2016)

FTag: Gatys-et-al-2016

APA7: Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image Style Transfer Using Convolutional Neural Networks. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2414–2423. https://doi.org/10.1109/CVPR.2016.265

Rendering the semantic content of an image in different styles is a difficult image processing task.
NSTProblematic

ack of image representations that explicitly represent semantic in- formation

use image representations derived from Convolutional Neural Networks optimised for object recognition
NSTMetho

A Neural Algorithm of Artistic Style that can separate and recombine the image content and style of natural images.
NSTConcept

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