Generative Adversarial Network (GAN)

Two neural networks trained against each other to generate data

Overview

A GAN consists of two neural networks trained in opposition to each other to generate new, synthetic data that resembles real-world examples.

What is a GAN?

A GAN is composed of two neural networks that work against each other:

  • A generator network that creates new data
  • A discriminator network that evaluates the generated data

How does it work?

The two networks engage in a continuous training process:

  • The generator creates new data samples from random noise
  • The discriminator evaluates both real and generated samples
  • The generator improves based on the discriminator's feedback
  • The discriminator gets better at detecting fake samples

Applications

GANs can be used to generate various types of synthetic data:

  • Realistic images and artwork
  • Video sequences
  • Text-to-image conversions
  • Data augmentation for training other AI models

Core Components

Generator Network
  • Takes random noise as input
  • Produces synthetic data samples
  • Learns to create increasingly realistic outputs
  • Aims to fool the discriminator
Discriminator Network
  • Receives both real and generated samples
  • Classifies inputs as real or fake
  • Provides feedback to the generator
  • Acts as a learned loss function