fast-neural-style

GitHub describes it as Feedforward style transfer. The repository metadata lists Lua as its primary language. This article stays within the project description and details documented in the GitHub repository README.

GitHub describes it as Feedforward style transfer. The repository metadata lists Lua as its primary language. The README describes the project this way: Perceptual Losses for Real-Time Style Transfer and Super-Resolution Justin Johnson, Alexandre Alahi, Presented at ECCV 2016

The README section "fast-neural-style" states: The paper builds on 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.

The README section "fast-neural-style" states: 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.

The README section "fast-neural-style" states: Stylizing this image of the Stanford campus at a resolution of 1200x630 takes 50 milliseconds on a Pascal Titan X:

The repository README is the source for this review. It does not replace a local installation or an independent test.

• Source 1 · Project repository
• Source 2 · README