The supervised learning algorithms that we focused on for most of this book receive input data that's typically complex and predicts a numerical or categorical label that we can compare to the ground truth to evaluate its performance. These algorithms are also called discriminative models because they learn to differentiate between different output classes.
How GANs work
How generative and discriminative models differ
The goal of generative models is to produce complex output, such as realistic images, given simple input, which can even be random numbers. They achieve this by modeling a probability distribution over the possible output. This probability distribution can have many dimensions, for example, one for each...
