What are generative adversarial networks (GANs), and how do they generate realistic synthetic data?

Generative adversarial networks (GANs) are a type of deep learning model composed of two neural networks: a generator and a discriminator. The generator network generates synthetic data samples, while the discriminator network evaluates the authenticity of these samples. Through adversarial training, the generator learns to generate increasingly realistic data distributions by fooling the discriminator. GANs are used in tasks such as image generation, data augmentation, and style transfer.