What is Generative Adversarial Network?

Generative Adversarial Network

Definition A deep learning framework consisting of two networks — a generator and a discriminator — that compete against each other, pushing the generator to produce increasingly realistic synthetic data. Introduced by Ian Goodfellow in 2014, GANs were the dominant generative image model before diffusion models.

In Depth

The generator creates fake samples trying to fool the discriminator, while the discriminator tries to distinguish real from fake. This adversarial dynamic drives both networks to improve. GANs excel at high-resolution image synthesis but are notoriously difficult to train due to instability and mode collapse. Variants like StyleGAN and BigGAN advanced photorealistic face and scene generation.

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