Generative adversarial networks (GANs) are a particular kind of machine learning models that are designed to learn a distribution of data from a representative sample. After training, you can sample from the distribution, generating novel examples that were not present in the original training set. So, for example, you can “show” a GAN a bunch of images of faces and then the GAN will be able to generate new faces. This is exactly what’s behind the curtains in the famous “this person does not exist” website.

GANs have seen a lot of improvement in the last few years since their…

Benjamín Machín

Machine Learning Engineer

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