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https://github.com/crypdick/awesome-neural-art

A curated list of awesome neural network-based art resources.
https://github.com/crypdick/awesome-neural-art

List: awesome-neural-art

awesome awesome-list image-transfer neural-art neural-doodles neural-network style-transfer texture-nets

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A curated list of awesome neural network-based art resources.

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# Awesome Neural Art [![Awesome](https://awesome.re/badge-flat2.svg)](https://awesome.re)



Creating art and manipulating images using deep neural networks.



## Contents



- [Colorization](#colorization)

- [Style Transfer](#style-transfer)

- [Super Resolution](#super-resolution)

- [Patching](#patching)

- [Background subtraction](#background-subtraction)

- [Deep Dream](#deep-dream)

- [Interactive art](#interactive-art)

- [Multiple categories](#multiple-categories)

- [Lectures](#lectures)



## Colorization



See also: [Awesome Image Colorization](https://github.com/MarkMoHR/Awesome-Image-Colorization) and the [colorization subreddit](https://www.reddit.com/r/Colorization) where people do this manually.



- [Interactive Deep Colorization](https://github.com/junyanz/interactive-deep-colorization)



  






- [colornet](https://github.com/pavelgonchar/colornet)



  



- [Colorful Image Colorization](https://github.com/richzhang/colorization)



  



## Style Transfer



- [neural-style](https://github.com/jcjohnson/neural-style) - Torch implementation of neural style algorithm.



  



- [adaptive-style-transfer](https://github.com/tensorlayer/adaptive-style-transfer) - Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization.

- [deep-photo-styletransfer](https://github.com/luanfujun/deep-photo-styletransfer) - Code and data for ["Deep Photo Style Transfer"](https://arxiv.org/abs/1703.07511).

- [fast-style-transfer](https://github.com/lengstrom/fast-style-transfer#image-stylization) - TensorFlow CNN for fast style transfer with larger scale style features in transformations.

- [texture_nets](https://github.com/DmitryUlyanov/texture_nets) - Code for "Texture Networks: Feed-forward Synthesis of Textures and Stylized Images". 

- [AdaIN-style](https://github.com/xunhuang1995/AdaIN-style) - Code for [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://arxiv.org/abs/1703.06868). Contains neat features such as transfering style without color, style interpolation, and spatial control.



  

  



- [Bicycle GAN](https://github.com/junyanz/BicycleGAN) - NIPS 2017 Toward Multimodal Image-to-Image Translation, PyTorch implementation. Given the same night image, model is able to synthesize possible day images with different types of lighting, sky and clouds.



  



- [Neural Style Transfer](https://github.com/titu1994/Neural-Style-Transfer) - Keras Implementation of Neural Style Transfer from the paper ["A Neural Algorithm of Artistic Style"](http://arxiv.org/abs/1508.06576) in Keras 2.0+.

- [Deep Painterly Harmonization](https://github.com/luanfujun/deep-painterly-harmonization) - Photoshopping an object into a painting, and then neural network changes the style (pallete, strokes, luminosity, etc) of the object to match the painting style. [See paper](https://arxiv.org/abs/1804.03189).



## Super Resolution



- [waifu2x](https://github.com/nagadomi/waifu2x) - Image Super-Resolution for Anime-Style Art.



  



- [srgan](https://github.com/tensorlayer/srgan) - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.



  



- [fft-descreen](https://github.com/6o6o/fft-descreen) - Clean up deconvolution checkerboard artifacts found in style transferred images.



## Patching



AKA in-painting



- [DeepFill V1](https://github.com/JiahuiYu/generative_inpainting) - Generative Image Inpainting with Contextual Attention.

- [EdgeConnect](https://github.com/knazeri/edge-connect) - a two-stage adversarial model that comprises of an edge generator followed by an image completion network.



- [Deep Image Completion](https://github.com/adamstseng/general-deep-image-completion) - Contains face-completion and general image completion models. Each can complete images with differnet types of corrupted masks like scribbles, lines, dots and texts.




## Background subtraction



AKA image matting. See also: [Awesome Background Subtraction](https://github.com/murari023/awesome-background-subtraction)



- Deep Image Matting - Implementations of the [Deep Image Matting paper](https://sites.google.com/view/deepimagematting).

  - [Dataset generator](https://github.com/hector-sab/DIM_DataCreation) - Generates training data for the Deep Image Matting paper.

  - [Tensorflow implementation](https://github.com/Joker316701882/Deep-Image-Matting) - Original paper author.

  - [PyTorch implementation](https://github.com/foamliu/Deep-Image-Matting-v2) - Same author as [Keras implementation](https://github.com/foamliu/Deep-Image-Matting), but this is their improved (and maintained) codebase.

  - [Another PyTorch implementation](https://github.com/huochaitiantang/pytorch-deep-image-matting)



- [Salient Object Detection](https://github.com/Joker316701882/Salient-Object-Detection) - Tensorflow implementation for cvpr2017 paper ["Deeply Supervised Salient Object Detection with Short Connections"](https://arxiv.org/abs/1611.04849) (not peer reviewed). Same author as original Deep Image Matting author.

- [AlphaGAN](https://github.com/CDOTAD/AlphaGAN-Matting) - Unofficial implementation of [AlphaGAN: Generative adversarial networks for natural image matting](https://arxiv.org/pdf/1807.10088.pdf). Uses a GAN as the name suggests.

- [DeepLab V3+](https://github.com/jfzhang95/pytorch-deeplab-xception) - Implementations of an encoder-decoder Modified Aligned Xception and ResNet as backbone [(paper)](https://arxiv.org/pdf/1802.02611.pdf).



## Deep Dream



- [deepdream](https://github.com/google/deepdream) - The original Google implementation.



## Interactive art



- [neural-doodle](https://github.com/alexjc/neural-doodle) - 

Turn your two-bit doodles into fine artworks with deep neural networks, generate seamless textures from photos, transfer style from one image to another, perform example-based upscaling, but wait... there's more! (An implementation of Semantic Style Transfer.) 



  



- [iGAN](https://github.com/junyanz/iGAN) - iGAN: Interactive Image Generation via Generative Adversarial Networks.



  



- [style2paints](https://github.com/lllyasviel/style2paints/) - An AI-driven interactive line art colorization tool. Backend is a Residual U-net and Auxiliary Classifier GAN.



## Multiple categories



- [deep-image-prior](https://github.com/DmitryUlyanov/deep-image-prior) -  Image restoration with neural networks but without learning. Does artifact removal, inpainting, super-resolution, denoising.



## Lectures



- [The Neural Aesthetic @ ITP-NYU, Fall 2018](https://ml4a.github.io/classes/itp-F18/)

- [Neural Style Transfer Review](https://github.com/ycjing/Neural-Style-Transfer-Papers) - Repo for the [Neural Style Transfer review article](https://arxiv.org/abs/1705.04058).



## License



[![CC0](http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](https://creativecommons.org/publicdomain/zero/1.0/)



To the extent possible under law, [Richard Decal](https://www.richarddecal.com) has waived all copyright and related or neighboring rights to this work.