Explore key concepts, practice flashcards, and test your knowledge — then unlock the full study pack.
In this module, we introduce Generative Adversarial Networks (GANs), which consist of two competing neural networks known as the generator and discriminator. This adversarial architecture allows GANs to create new data instances closely resembling the original dataset, marking a significant evolution in generative modeling.
The training process for GANs is characterized by an adversarial game where the generator aims to improve its output to fool the discriminator, while the discriminator enhances its ability to identify real versus generated samples.
What is a Generative Adversarial Network (GAN)?
A framework consisting of two neural networks: a generator and a discriminator, that compete with each other in a game-theoretic setting.
What role does the generator play in a GAN?
The generator creates new data samples from random noise, aiming to resemble the training data it was trained on.
How does the discriminator function in GANs?
The discriminator evaluates the authenticity of the samples produced by the generator, determining whether they are real (from the training dataset) or fake (generated).
Click any card to reveal the answer
Q1
What two neural networks make up a GAN?
Q2
What is the main goal of the generator in GANs?
Q3
What does the discriminator in GANs primarily do?
Upload your own notes, PDF, or lecture to get complete study notes, dozens of flashcards, and a full practice exam like the one above — generated in seconds.
Sign Up Free → No credit card required • 1 free study pack included