👉 The Generative Adversarial Network (GAN) is a class of machine learning frameworks designed to generate new data instances that resemble existing data. It consists of two neural networks, the generator and the discriminator, which work in tandem through adversarial training. The generator creates synthetic data samples, while the discriminator evaluates them against real data, aiming to distinguish between the two. Over time, this competition pushes both networks to improve: the generator becomes better at producing realistic samples, and the discriminator becomes more adept at identifying fakes. This process results in the generator eventually producing high-quality, synthetic data that can mimic the statistical properties of the original dataset.
