We implement the style transfer technique from "Image Style Transfer Using Convolutional Neural Networks" (Gatys et al., CVPR 2015).
The general idea is to take two images, and produce a new image that reflects the content of one but the artistic "style" of the other. We will do this by first formulating a loss function that matches the content and style of each respective image in the feature space of a deep network, and then performing optimization on the pixels of the image itself.
The deep network we use as a feature extractor is SqueezeNet, a small model that has been trained on ImageNet.
For detailed overview please review the notebook file.