What are GANs?

Generative Adversarial Networks (GANs) are a class of unsupervised learning models consisting of two neural networks:

  • Generator: Creates synthetic data (e.g., images) from random noise
  • Discriminator: Judges whether data is real or fake

🎯 Key Concept: The two networks compete in a zero-sum game, improving through adversarial training

How GANs Work

  1. Training Process

    • Generator produces fake samples
    • Discriminator evaluates real vs fake
    • Loss function updates both networks

  2. Mathematical Foundation

    • Minimax game: $ \mathcal{L}(G,D) = \mathbb{E}{x\sim p{data}}[\log D(x)] + \mathbb{E}_{z\sim p_z}[ \log(1 - D(G(z)))] $

  3. Common Architectures

    • Vanilla GAN (basic version)
    • DCGAN (with convolutional layers)
    • StyleGAN (for high-quality image generation)

Applications of GANs

  • 🖼️ Image synthesis (e.g., face generation)
  • 🎨 Art creation & style transfer
  • 🧠 Data augmentation for machine learning
  • 📊 Anomaly detection in datasets

Expand Your Knowledge

For deeper understanding, check our GANs Advanced Topics tutorial.

GAN Structure
StyleGAN Example
GAN Application Samples