Generative Adversarial Network

An architecture consisting of two neural networks trained in opposition to each other: a generator that creates synthetic data, and a discriminator that tries to distinguish real data from generated data. Through this adversarial process, the generator learns to produce increasingly realistic outputs. GANs were the dominant approach for image generation throughout the late 2010s before diffusion models rose to prominence.
See also: diffusion model, generative AI, synthetic media.