· 2016
Image Style Transfer Using Convolutional Neural Networks
- 6,029citations
- 2016year
Short summary
A novel algorithm separates and recombines image content and style using representations from Convolutional Neural Networks (CNNs), producing new images with high perceptual quality that merge arbitrary photos with artistic styles.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces 'A Neural Algorithm of Artistic Style' for image style transfer.
- Utilizes high-level image representations from Convolutional Neural Networks (CNNs).
- Separates and recombines image content and artistic style.
- Generates new images of high perceptual quality by merging arbitrary photos with artwork styles.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source., 2016 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science