GANs consist of two neural networks that operate in tandem, engaging in a game-like competition. The architecture includes a generator network that creates data samples (such as images) and a discriminator network that evaluates these samples, distinguishing between genuine data and those generated by the generator. The generator aims to produce increasingly convincing data to fool the discriminator, while the discriminator continuously improves its ability to identify generated data. This adversarial process drives both networks to improve, ultimately leading to the generation of highly realistic data by the generator. GANs have since become a foundational technique in the field of machine learning, particularly in image synthesis, data augmentation, and various creative applications.