Exploring Generative Adversarial Networks (GANs) in Two-Dimensional Space

The Figure 3 below shows that GAN comprises of two main parts: generator and discriminator. As the name suggests, generator is responsible to generate new (fake) samples while discriminator attempts to distinguish the real and fake ones. The main objective of training a GAN is to make the generator able to generate new samples such that those samples are indistinguishable by the discriminator. Once this happens, it means that our generator is now able to create samples which the quality is already as good as the originals. As I've mentioned earlier, we are going to work on two-dimensional data since it is a lot simpler as compared to the MNIST dataset we saw earlier.