What is the main function of a Generative Adversarial Network (GAN)?

The main function of a Generative Adversarial Network (GAN) is to create new data from existing data. GANs consist of two neural networks, the generator and the discriminator, that work in opposition to each other. The generator creates new samples, attempting to mimic the distribution of the training data, while the discriminator evaluates the samples to determine if they are real (from the original dataset) or fake (from the generator). This process continues until the generator produces data that is indistinguishable from real data according to the discriminator.

In essence, GANs are powerful tools for generating new content, such as images, music, or text, which makes them particularly useful in fields like art generation, photo enhancement, and simulation of complex systems. This generative capability differentiates GANs from other model types that are more focused on classification or regression tasks, and it illustrates their unique position within the landscape of artificial intelligence techniques.