What are GANs?
GANs (Generative Adversarial Networks) are a class of deep learning models designed to generate new data that resembles existing data. They consist of two neural networks:
- Generator: Creates synthetic data (e.g., images, text)
- Discriminator: Evaluates whether data is real or fake
This adversarial process enables the model to iteratively improve its ability to produce realistic outputs. 🧠⚔️
Key Concepts
- Adversarial Training: Generator and Discriminator compete in a zero-sum game
- Loss Function: Balances generation quality and discrimination accuracy
- Mode Collapse: A common issue where the generator produces limited varieties of outputs
📌 Tip: For visual demonstrations, check our GANs Visualization Tool
Applications
GANs are widely used in:
- Image synthesis (e.g., creating realistic faces)
- Data augmentation
- Artistic style transfer
- Video generation
Further Reading
If you're interested in diving deeper:
Explore the world of generative models with our comprehensive resources! 🚀