A Neural Algorithm of Artistic Style

In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery.

[1]  E H Adelson,et al.  Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[2]  James R. Bergen,et al.  Texture Analysis: Representation and Matching , 1995, ICIAP.

[3]  Joshua B. Tenenbaum,et al.  Separating Style and Content with Bilinear Models , 2000, Neural Computation.

[4]  David Salesin,et al.  Image Analogies , 2001, SIGGRAPH.

[5]  Alexei A. Efros,et al.  Image quilting for texture synthesis and transfer , 2001, SIGGRAPH.

[6]  Michael Ashikhmin,et al.  Fast Texture Transfer , 2003, IEEE Computer Graphics and Applications.

[7]  Eero P. Simoncelli,et al.  A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients , 2000, International Journal of Computer Vision.

[8]  Ahmed M. Elgammal,et al.  Separating style and content on a nonlinear manifold , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[9]  Seah Hock Soon,et al.  Feature Guided Texture Synthesis (FGTS) for artistic style transfer , 2007, DIMEA.

[10]  Kyunghyun Yoon,et al.  Directional texture transfer , 2010, NPAR.

[11]  J. Collomosse,et al.  State of the ‘Art’: A Taxonomy of Artistic Stylization Techniques for Images and Video (cid:63) , 2012 .

[12]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[13]  Tobias Isenberg,et al.  State of the "Art”: A Taxonomy of Artistic Stylization Techniques for Images and Video , 2013, IEEE Transactions on Visualization and Computer Graphics.

[14]  Ha Hong,et al.  Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.

[15]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[16]  Daniel L. K. Yamins,et al.  Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition , 2014, PLoS Comput. Biol..

[17]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Trevor Darrell,et al.  Recognizing Image Style , 2013, BMVC.

[19]  Nikolaus Kriegeskorte,et al.  Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation , 2014, PLoS Comput. Biol..

[20]  Leon A. Gatys,et al.  Texture Synthesis Using Convolutional Neural Networks , 2015, NIPS.

[21]  Andrea Vedaldi,et al.  Understanding deep image representations by inverting them , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Matthias Bethge,et al.  Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet , 2014, ICLR.

[23]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[24]  Marcel A. J. van Gerven,et al.  Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream , 2014, The Journal of Neuroscience.

[25]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[26]  Leon A. Gatys,et al.  Texture synthesis and the controlled generation of natural stimuli using convolutional neural networks , 2015, ArXiv.