Neural style transfer and deep photo style transfer are interesting fields of deep learning. Their popularity has grown to an another level. Apps like Prisma and Deepart.io accelerated the popularity. If you are working with neural style transfer or deep photo style transfer these are some very important resources(papers, implementations and tutorials) to help you out.

Research Papers

A Neural Algorithm of Artistic Style

Neural Style Transfer — A Review

Deep Photo Style Transfer

Controlling Perceptual Factors in Neural Style Transfer

Instance Normalization: The Missing Ingredient for Fast Stylization

Implementations

Torch implementation of Neural Style Transfer

Tensorflow Implementation of Neural Style Transfer

Torch implementation of Deep Photo Style Transfer

Tensorflow implementation of Deep Photo Style Transfer

Tensorflow implementation of Fast Style Transfer

Torch7(Lua) implementation of Neural Style Transfer

Keras implementation of Neural Style Transfer

Theano+Keras implementation of style transfer algorithms

DeepPy implementation of Neural Artisitic Style

pyCaffe implementation of Neural Artistic Style

Caffe implementation of Neural Style Transfer

MXNet implementation of Neural Style Transfer

Chainer implementation of Neural Style Transfer

MXNet pre-trained model for Neural Style Transfer

https://github.com/dmlc/web-data/raw/master/mxnet/art/model.zip

Tutorials

Video Tutorials

Articles

Companies using Neural Style Transfer/Deep Photo Style Transfer

Docker implementation of jcjohnson neural-style code

https://hub.docker.com/r/ffedoroff/neural-style/

Audio Texture Synthesis and style transfer

Reddit Thread

Mathematica(StackExchange)

Thank you for reading. If you want to get into contact, you can reach out to me at ahikailash1@gmail.com

About Me:

I am a Co-Founder of MateLabs, where we have built Mateverse, an ML Platform which enables everyone to easily build and train Machine Learning Models, without writing a single line of code.

Note: Recently, I published a book on GANs titled “Generative Adversarial Networks Projects”, in which I covered most of the widely popular GAN architectures and their implementations. DCGAN, StackGAN, CycleGAN, Pix2pix, Age-cGAN, and 3D-GAN have been covered in details at the implementation level. Each architecture has a chapter dedicated to it. I have explained these networks in a very simple and descriptive language using Keras framework with Tensorflow backend. If you are working on GANs or planning to use GANs, give it a read and share your valuable feedback with me at ahikailash1@gmail.com