Generative models are a subset of artificial intelligence that focuses on creating new data instances that resemble existing data. Unlike traditional AI models that classify or predict, generative models aim to produce new content, such as images, music, text, and more. One of the pioneering architectures in this realm is the Generative Adversarial Network (GAN).
A GAN consists of two neural networks: the generator and the discriminator. The generator generates data, while the discriminator evaluates it. Through a continuous feedback loop, the generator improves its creations until the discriminator can hardly distinguish between real and generated data. This results in the generation of remarkably authentic content, such as images that look like they were taken by a professional photographer or text that reads like a human-authored article.