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TensorFlow (Python API) implementation of Neural Style
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# neural-style-tf This is a TensorFlow implementation of several techniques described in the papers: * [Image Style Transfer Using Convolutional Neural Networks](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf) by Leon A. Gatys, Alexander S. Ecker, Matthias Bethge * [Artistic style transfer for videos](https://arxiv.org/abs/1604.08610) by Manuel Ruder, Alexey Dosovitskiy, Thomas Brox * [Preserving Color in Neural Artistic Style Transfer](https://arxiv.org/abs/1606.05897) by Leon A. Gatys, Matthias Bethge, Aaron Hertzmann, Eli Shechtman Additionally, techniques are presented for semantic segmentation and multiple style transfer. The Neural Style algorithm synthesizes a [pastiche](https://en.wikipedia.org/wiki/Pastiche) by separating and combining the content of one image with the style of another image using convolutional neural networks (CNN). Below is an example of transferring the artistic style of [The Starry Night](https://en.wikipedia.org/wiki/The_Starry_Night) onto a photograph of an African lion: <p align="center"> <img src="examples/lions/42_output.png" width="512"/> <img src="examples/lions/content_style.png" width="290"/> </p> Transferring the style of various artworks to the same content image produces qualitatively convincing results: <p align="center"> <img src="examples/lions/32_output.png" width="192"> <img src="examples/lions/styles/matisse_crop.jpg" width="192"/> <img src="examples/lions/33_output.png" width="192"/> <img src="examples/lions/styles/water_lilies_crop.jpg" width="192"/> <img src="examples/lions/wave_output.png" width="192"/> <img src="examples/lions/styles/wave_crop.jpg" width="192"/> <img src="examples/lions/basquiat_output.png" width="192"/> <img src="examples/lions/styles/basquiat_crop.jpg" width="192"/> <img src="examples/lions/calliefink_output.png" width="192"/> <img src="examples/lions/styles/calliefink_crop.jpg" width="192"/> <img src="examples/lions/giger_output.png" width="192"/> <img src="examples/lions/styles/giger_crop.jpg" width="192"/> </p> Here we reproduce Figure 3 from the first paper, which renders a photograph of the Neckarfront in Tübingen, Germany in the style of 5 different iconic paintings [The Shipwreck of the Minotaur](http://www.artble.com/artists/joseph_mallord_william_turner/paintings/the_shipwreck_of_the_minotaur), [The Starry Night](https://www.wikiart.org/en/vincent-van-gogh/the-starry-night-1889), [Composition VII](https://www.wikiart.org/en/wassily-kandinsky/composition-vii-1913), [The Scream](https://www.wikiart.org/en/edvard-munch/the-scream-1893), [Seated Nude](http://www.pablopicasso.org/seated-nude.jsp): <p align="center"> <img src="examples/gatys_figure/tubingen.png" height="192px"> <img src="examples/gatys_figure/tubingen_shipwreck.png" height="192px"> <img src="examples/initialization/init_style.png" height="192px"> <img src="examples/gatys_figure/tubingen_picasso.png" height="192px"> <img src="examples/gatys_figure/tubingen_scream.png" height="192px"> <img src="examples/gatys_figure/tubingen_kandinsky.png" height="192px"> </p> ### Content / Style Tradeoff The relative weight of the style and content can be controlled. Here we render with an increasing style weight applied to [Red Canna](http://www.georgiaokeeffe.net/red-canna.jsp): <p align="center"> <img src="examples/style_content_tradeoff/okeffe.jpg" height="160px"> <img src="examples/style_content_tradeoff/okeffe_10.png" width="160px"> <img src="examples/style_content_tradeoff/okeffe_100.png" width="160px"> <img src="examples/style_content_tradeoff/okeffe_10000.png" width="160px"> <img src="examples/style_content_tradeoff/output_1000000.png" width="160px"> </p> ### Multiple Style Images More than one style image can be used to blend multiple artistic styles. <p align="center"> <img src="examples/multiple_styles/tubingen_starry_scream.png" height="192px"> <img src="examples/multiple_styles/tubingen_scream_kandinsky.png"
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:603547263c9f860b, topic:deep-learning, topic:tensorflow
