Generative Adversarial Networks (GANs) are a class of neural networks that generate new data with a probability distribution that has the same statistics as some real-world dataset. This tutorial will provide an overview of GANs, their architecture, and how they work.

Key Components of GANs

1. Generator

The generator is a neural network that tries to generate new data with the same distribution as the real data. It takes a random noise vector as input and outputs a data sample.

2. Discriminator

The discriminator is another neural network that tries to distinguish between real data and generated data. It takes a data sample as input and outputs a probability that the sample is real.

Training Process

The training process of a GAN involves two main steps:

  1. Train the Discriminator: The discriminator is trained on real data samples. The generator is not involved in this step.
  2. Train the Generator: The generator is trained to fool the discriminator. It takes a random noise vector as input and outputs a data sample. The discriminator then evaluates the generated sample.

Challenges and Limitations

GANs have several challenges and limitations:

  • Mode Collapse: The generator may produce only a few types of samples, ignoring the diversity of the real data.
  • Unrealistic Outputs: GANs may generate samples that look realistic but lack the complexity of real-world data.

Resources

For further reading on GANs, check out the following resources:

GAN Architecture