What is a Generative Adversarial Network (GAN)?

A Generative Adversarial Network (GAN) is accurately described as a framework that consists of two key components: a generator and a discriminator. The generator's role is to create data that mimics the real data, while the discriminator's job is to evaluate the data and distinguish between real and generated samples.

This adversarial process involves the generator attempting to improve its ability to produce realistic outputs, while the discriminator concurrently enhances its skill in identifying fakes. This ongoing competition between the two networks leads to the generator producing increasingly realistic data over time, making GANs a highly effective and popular approach in the field of generative modeling.

The other options misrepresent what a GAN is. A single neural network would not fully encompass the concept of a GAN, which requires the interaction of both the generator and discriminator. A database management system refers to software used for creating and managing databases, which is unrelated to the functioning of GANs. Lastly, a type of reinforcement learning model does not describe GANs accurately, as they operate within a different paradigm that focuses on competitive learning rather than learning from rewards in an environment.