Synthetic Image Generation with GANs
Explored Generative Adversarial Networks for synthetic image generation using the classic generator-discriminator training setup.
Model Structure
| Component | Role |
|---|---|
| Generator | Converts random noise into synthetic images |
| Discriminator | Classifies images as real or generated |
| Training goal | Make generated images realistic enough to fool the discriminator |
Key Idea
The discriminator learns to separate real training images from fake images, while the generator learns to produce increasingly realistic images through this adversarial feedback loop.
