{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-neural-algorithm-of-artistic-style","title":"A Neural Algorithm of Artistic Style","arxiv_id":"1508.06576","date":"2015-08-26","proceeding":null,"authors":["Leon A. Gatys","Alexander S. Ecker","Matthias Bethge"],"abstract":"In fine art, especially painting, humans have mastered the skill to create\nunique visual experiences through composing a complex interplay between the\ncontent and style of an image. Thus far the algorithmic basis of this process\nis unknown and there exists no artificial system with similar capabilities.\nHowever, in other key areas of visual perception such as object and face\nrecognition near-human performance was recently demonstrated by a class of\nbiologically inspired vision models called Deep Neural Networks. Here we\nintroduce an artificial system based on a Deep Neural Network that creates\nartistic images of high perceptual quality. The system uses neural\nrepresentations to separate and recombine content and style of arbitrary\nimages, providing a neural algorithm for the creation of artistic images.\nMoreover, in light of the striking similarities between performance-optimised\nartificial neural networks and biological vision, our work offers a path\nforward to an algorithmic understanding of how humans create and perceive\nartistic imagery.","url_abs":"http://arxiv.org/abs/1508.06576v2","url_pdf":"http://arxiv.org/pdf/1508.06576v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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