Nvidia’s research team proposed StyleGAN at the end of 2018, and instead of trying to create a fancy new technique to stabilize GAN training or introducing a new architecture, the paper says that their technique is “orthogonal to the ongoing discussion about GAN loss functions, regularization, and hyper-parameters.”

They also discuss some of the clever techniques (Adaptive Instance Normalization, Style Mixing, Stochastic Noise, Mapping Network) that you’d need to generate images that you exactly want.

This blog covers these topics and contains all the code you need to experiment on your images (Might be interesting to see what you’d look like when you grow old): https://blog.nanonets.com/stylegan-got

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