A Generative Adversarial Network (GAN) consists of two neural networks: a generator and a discriminator. The generator creates fake data, while the discriminator tries to distinguish between real and fake data. The two networks are trained together in a process called adversarial training.
The generator improves by trying to create more realistic data to fool the discriminator, while the discriminator gets better at detecting fake data. This iterative process leads to the generator creating increasingly convincing synthetic data, such as images, text, or audio.
GANs are used in applications like image generation, video creation, and data augmentation. They are particularly useful for generating high-quality, realistic outputs in creative tasks.