GAN (Generative Adversarial Network)
A generative model architecture where two networks — a generator and discriminator — compete to produce realistic synthetic data.
Definition
Introduced by Ian Goodfellow in 2014, GANs consist of two neural networks trained in opposition: a Generator that creates synthetic data, and a Discriminator that tries to distinguish real from fake. The generator learns to fool the discriminator; the discriminator learns to catch fakes. This adversarial game drives both networks toward high-quality synthesis.
GANs were the dominant image generation approach from ~2015–2022, producing photorealistic faces (StyleGAN), deepfakes, data augmentation, and image-to-image translation (pix2pix, CycleGAN). They are difficult to train (mode collapse, training instability) and have largely been superseded by diffusion models for most tasks.
GANs remain useful for real-time inference (they generate in a single forward pass vs. iterative diffusion) and medical imaging where training data is limited.
Examples
- StyleGAN (face synthesis)
- CycleGAN (image translation)
- BigGAN
- Deepfake tools
Related Terms
Generative AI
AI systems that create new content — text, images, audio, video, code — by learning patterns from training data.
Diffusion Model
A generative model that learns to create data by reversing a gradual noise-addition process.
Neural Network
A computing system of interconnected nodes inspired by biological brains, trained to recognise patterns.