Arbitrary style transfer in real time
In this section, we will learn how to implement a network that could perform arbitrary style transfer in real time. We have already learned how to use a feed-forward network for faster inference and that solves the real-time part. We have also learned how to use conditional instance normalization to transfer a fixed number of styles. Now, we will learn one further normalization technique that allows for any arbitrary style, and then we are good to go in terms of implementing the code.
Implementing adaptive instance normalization
Like CIN, AdaIN is also instance normalization, meaning that the mean and standard deviation are calculated across (H, W) per image, and per channel, as opposed to batch normalization, which calculates across (N, H, W). In CIN, the gammas and betas are trainable variables, and they learn the means and variances that are needed for different styles. In AdaIN, gammas and betas are replaced by standard deviations and...
