A Neural Algorithm of Artistic Style
Open accessAbstract
In fine art, especially painting, humans have mastered the skill to create unique visual experiences by composing a complex interplay between the content and style of an image. The algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. Recently, a class of biologically inspired vision models called Deep Neural Networks have demonstrated near-human performance in complex visual tasks such as object and face recognition. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system can separate and recombine the content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. In light of recent studies using fMRI and electrophysiology that have shown striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path towards an algorithmic understanding of how humans create and perceive artistic imagery. The algorithm introduces a novel class of stimuli that could be used to test specific computational hypotheses about the perceptual processing of artistic style. Meeting abstract presented at VSS 2016
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Gatys and colleagues showed that visual features of models can be combined to represent image styles.
“Gatys et al. [156] demonstrated that visual features of models could be combined to represent image styles.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [156]
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- Authors
- Gatys, L., Ecker, A., & Bethge, M.
- Affiliations
- University of Tübingen (DE)
- Venue
- Journal of Vision
- Published
- 2016-09-01
- Volume
- 16
- Issue
- 12
- Pages
- 326
- ISSN
- 1534-7362 (ISSN-L)
- OpenAlex
- W1924619199
- Cited by
- 2,612
- References
- 22
- Citations by year
- 2026: 86 · 2025: 205 · 2024: 257 · 2023: 313 · 2022: 270 · 2021: 356 · 2020: 319 · 2019: 309 · 2018: 225 · 2017: 164 · 2016: 102 · 2015: 5 · 2013: 1
- FWCI
- 179.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Vision (publishedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- arXiv (Cornell University)
- Nature Communications
- Funders
- Studienstiftung des Deutschen Volkes
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Life Sciences › Neuroscience › Cognitive Neuroscience › Aesthetic Perception and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- artistic style transfer, content and style separation, deep neural network, artistic image generation, perceptual quality
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