GANs are a class of machine learning frameworks designed to generate new data that mimics the training data. They consist of two neural networks: a generator and a discriminator. Here's a breakdown of key concepts and practices:
📌 Core Principles
- Generator: Creates synthetic data (e.g., images, text) by learning patterns from the training set.
- Discriminator: Evaluates generated data to distinguish between real and fake samples.
- Adversarial Process: The generator and discriminator compete in a zero-sum game to improve quality iteratively.
🧰 Training Essentials
Loss Functions
- Use binary cross-entropy for basic GANs
- Consider Wasserstein distance for improved stability
- Add gradient penalty for Lipschitz constraint enforcement
Optimization Techniques
- Adam optimizer with carefully tuned learning rates
- Weight clipping in Wasserstein GANs
- Gradient reversal layer for better training dynamics
Common Architectures
- DCGAN (Deep Convolutional GAN)
- StyleGAN for high-quality image generation
- CycleGAN for image-to-image translation tasks
📈 Performance Metrics
- Fréchet Inception Distance (FID): Measures similarity between generated and real images
- Inception Score: Evaluates diversity and quality of generated samples
- Mode Collapse Detection: Monitor for lack of variety in outputs
📚 Recommended Resources
- GAN Tutorial for foundational concepts
- Advanced Techniques for state-of-the-art methods
- Practical Implementation with code samples
For visual demonstrations of GAN architectures, explore this interactive visualization.