Style Transfer

Style Transfer is a process in which we strive to modify the style of an image while preserving its content. Given an input image and a style image, we can compute an output image with the original content but a new style.

Kaggle kernel

Check out corresponding Medium article:

Style Transfer - Styling Images with Convolutional Neural Networks

How does it work?

We take input image and style images and resize them to equal shapes.
We load a pre-trained CNN (VGG16).
Knowing that we can distinguish layers that are responsible for the style (basic shapes, colors etc.) and the ones responsible for the content (image-specific features), we can separate the layers to independently work on the content and style.
Then we set our task as an optimization problem where we are going to minimize:
- content loss (distance between the input and output images - we strive to preserve the content)
- style loss (distance between the style and output images - we strive to apply a new style)
- total variation loss (regularization - spatial smoothness to denoise the output image)
Finally, we set our gradients and optimize with the L-BFGS algorithm.