A Generative Adversarial Network (GAN) is a deep learning architecture where two neural networks (generator and discriminator) compete to create realistic synthetic data. The generator creates fake data while the discriminator tries to detect it, leading to increasingly realistic outputs.

NYD Application: Could be used to generate synthetic user data for testing, create design variations, or produce placeholder content for prototypes.

Example: "The GAN generated realistic user avatars for our design mockups without using real customer photos."