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Image Style Transfer Using Convolutional Neural Networks

Image Style Transfer Using Convolutional Neural NetworksLeon A. GatysCentre for Integrative Neuroscience, University of T ubingen, GermanyBernstein Center for Computational Neuroscience, T ubingen, GermanyGraduate School of Neural Information Processing, University of T ubingen, S. EckerCentre for Integrative Neuroscience, University of T ubingen, GermanyBernstein Center for Computational Neuroscience, T ubingen, GermanyMax Planck Institute for Biological Cybernetics, T ubingen, GermanyBaylor College of Medicine, Houston, TX, USAM atthias BethgeCentre for Integrative Neuroscience, University of T ubingen, GermanyBernstein Center for Computational Neuroscience, T ubingen, GermanyMax Planck Institute for Biological Cybernetics, T ubingen, GermanyAbstractRendering the semantic content of an Image in differentstyles is a difficult Image processing task.

improve the understanding of deep image representations [27 ,24]. In fact, our style transfer algorithm combines a parametric texture model based on Convolutional Neural Networks [10] with a method to invert their image repre-sentations [24]. 2. Deep image representations The results presented below were generated on the ba-

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