Neural networks aren’t limited to just learning data; they can also learn to create it. One of the classic machine learning papers is Generative Adversarial Networks  (GANs) (2014) by Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, and others. GANs take the form of two opposing neural networks one learning to generate fake samples while the other tries to separate the real samples from the fakes.

Sophisticated GAN models like VQGAN and others generate everything from fake landscapes, faces, and even Minecraft worlds.

These are my notes on the classic paper that first introduced GANs.

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