Generative Adversarial Networks are a type of neural network architecture invented in 2014 by Ian Goodfellow and his collaborators. GANs are foundational to much of today’s AI image generation.
A GAN is made of two neural networks that play a game:
| Component | Role |
|---|---|
| Generator (G) | Tries to create fake data that looks like real data (e.g., fake images). |
| Discriminator (D) | Tries to tell real from fake — it acts like a critic or detective. |
They train together:
- The generator creates an image.
- The discriminator decides if it’s fake or real.
- Feedback from the discriminator helps the generator improve.
- Over time, the generator gets so good the discriminator can’t tell the difference.
This is why it’s called adversarial — the two networks are in a constant battle.
GANs are unsupervised (or self-supervised) learning models — they don’t need labeled data.
They learn the distribution of training data and generate new data from that distribution.
Many improved GANs have been developed since 2014, including:
| Variant | Purpose |
|---|---|
| DCGAN (2015) | Deep Convolutional GAN — popular for image generation |
| StyleGAN (2018–2021) | Introduced “style” control — used in “This Person Does Not Exist” |
| CycleGAN | Image-to-image translation (e.g., horses ↔ zebras) |
| BigGAN | High-quality, class-conditional image generation (from ImageNet) |