fast-neural-style: Feedforward style transfer

This is the code for the paper Perceptual Losses for Real-Time Style Transfer and Super-Resolution, Justin Johnson, Alexandre Alahi, Li Fei-Fei.

The paper builds on A Neural Algorithm of Artistic Style by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge by training feedforward neural networks that apply artistic styles to images. After training, our feedforward networks can stylize images hundreds of times faster than the optimization-based method presented by Gatys et al.

This repository also includes an implementation of instance normalization as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization by Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. This simple trick significantly improves the quality of feedforward style transfer models.