Generative Adversarial Networks (GANs)
GANs consist of two neural networks—a Generator and a Discriminator—that compete against each other. The Generator creates fake data, while the Discriminator tries to distinguish real from fake.
Invented by Ian Goodfellow (2014) - GANs can generate photorealistic images, create art, and even synthesize voices.
How GANs Work
Generator
Takes random noise as input and generates fake data (e.g., images).
Goal: Fool the discriminator
Discriminator
Classifies data as real (from dataset) or fake (from generator).
Goal: Correctly identify fakes
Training Process
This simplified example shows the adversarial training loop.
python
Output:
Click "Run Code" to see output
Popular GAN Variants
- DCGAN: Deep Convolutional GAN for image generation
- StyleGAN: High-quality face generation with style control
- CycleGAN: Image-to-image translation without paired data
- Pix2Pix: Conditional GAN for paired image translation