Translation: A Generative Adversarial Network (GAN) is a two-part system in which one component creates new content and the other evaluates it, allowing the model to generate increasingly realistic outputs such as images, audio, or video. The generator produces new examples, while the discriminator examines these examples and determines whether they appear real or fake when compared with the training data. Through this iterative process, the generator improves its ability to produce more convincing content over time. GANs are commonly used to create synthetic images, deepfakes, and artwork.