Creating synthetic data with GANs

This book mostly focuses on supervised learning algorithms that receive input data and predict an outcome, which we can compare to the ground truth to evaluate their performance. Such algorithms are also called discriminative models because they learn to differentiate between different output values.

GANs are an instance of generative models like the variational autoencoder we encountered in the previous chapter. As described there, a generative model takes a training set with samples drawn from some distribution p_data and learns to represent an estimate p_model of that data-generating distribution.

As mentioned in the introduction, GANs are considered one of the most exciting recent machine learning innovations because they appear capable of generating high-quality samples that faithfully mimic a range of input data. This is very attractive given the absence or high cost of labeled data required for supervised learning.

GANs have triggered...