Generative Adversarial Networks (GANs) are a class of neural networks that generate new data with a similar distribution to real-world data. They consist of two main components: a generator and a discriminator.

How GANs Work

  • Generator: This network generates new data instances. It takes random noise as input and tries to create data that looks like real instances from the training dataset.
  • Discriminator: This network tries to distinguish between real data and generated data. It takes either real data or generated data as input and outputs a probability that the input is real.

Here's a simplified illustration of how GANs work:

  • GAN Process

Training a GAN

Training a GAN is an iterative process. The generator and discriminator are trained simultaneously in alternating steps:

  1. Generator Training: The generator is trained to produce data that is indistinguishable from real data.
  2. Discriminator Training: The discriminator is trained to distinguish between real and generated data.

This process continues until the generator produces data that is indistinguishable from real data.

Applications of GANs

GANs have various applications, including:

  • Image Generation: Creating new images that look like real images.
  • Image Editing: Editing existing images in various ways, such as removing objects or changing the style.
  • Video Generation: Generating new videos with realistic content.
  • Text Generation: Creating new text that is similar to real text.

For more information on GANs, you can check out our Deep Learning Tutorial.