Train Generative Adversarial Network (GAN) - MATLAB & Simulink

This example shows how to train a generative adversarial network (GAN) to generate images. A generative adversarial network (GAN) is a type of deep learning network that can generate data with similar characteristics as the input real data. Generator -- Given a vector of random values (latent inputs) as input, this network generates data with the same structure as the training data. Discriminator -- Given batches of data containing observations from both the training data, and generated data from the generator, this network attempts to classify the observations as "real" or "generated". Train the generator to generate data that "fools" the discriminator.