Real-time Artwork Generation using Deep Learning

In this post we will be looking into the paper "Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization"(AdaIN) by Huang et. We are looking this paper because it had some key advantages over the other state-of-the-art methods at the time or release. Most important of all, this method, once trained, can be used to transfer style between any arbitrary content-style image pair, even ones not seen during training. While the method proposed by Gatys et. The AdaIN method is also flexible, it allows for control over the strength of the transferred style in the stylised image and also allows for extensions such as style interpolation and spatial controls.