Fast, Lightweight Style Transfer using Deep Learning
Fast, Lightweight Style Transfer using Deep Learning: A re-implementation of "A Learned Representation For Artistic Style" (which proposed using Conditional Instance Normalization), "Instance Normalization: The Missing Ingredient for Fast Stylization", and the fast neural-style transfer method proposed in "Perceptual Losses for Real-Time Style Transfer and Super-Resolution" using Lasagne and Theano.
This repository contains a re-implementation of the paper A Learned Representation For Artistic Style and its Google Magenta TensorFlow implementation. The major differences are as follows:
The following are the results when this technique was applied to style images described in the paper (to generate pastiches of a set of 32 paintings by various artists, and of 10 paintings by Monet, respectively):
This repository also contains a re-implementation of the paper Perceptual Losses for Real-Time Style Transfer and Super-Resolution and the author's torch implementation(fast-neural-style) with the following differences:
conv2_2
layer of the VGG-Net for the content loss as in the paper, as opposed to the conv3_3
layer as in the author's implementation.
This repository re-implements 3 research papers: