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Awesome Neural Art Awesome

Creating art and manipulating images using deep neural networks.



See also: Awesome Image Colorization and the colorization subreddit where people do this manually.

Style Transfer

  • neural-style - Torch implementation of neural style algorithm.

  • Bicycle GAN - 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.

Super Resolution

  • waifu2x - Image Super-Resolution for Anime-Style Art.

  • srgan - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.

  • fft-descreen - Clean up deconvolution checkerboard artifacts found in style transferred images.


AKA in-painting

  • DeepFill V1 - Generative Image Inpainting with Contextual Attention.
  • EdgeConnect - a two-stage adversarial model that comprises of an edge generator followed by an image completion network.

- [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

Deep Dream

  • deepdream - The original Google implementation.

Interactive art

  • 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 - iGAN: Interactive Image Generation via Generative Adversarial Networks.

  • style2paints - An AI-driven interactive line art colorization tool. Backend is a Residual U-net and Auxiliary Classifier GAN.

Multiple categories

  • deep-image-prior - Image restoration with neural networks but without learning. Does artifact removal, inpainting, super-resolution, denoising.




To the extent possible under law, Richard Decal has waived all copyright and related or neighboring rights to this work.


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