Dive into the world of Generative Adversarial Networks (GANs). Learn how these powerful AI models work, their applications, and the different types of GANs.
What is a generative adversarial network?
A GAN is a type of AI model that is trained to create fake data that resembles real data, such as fake images that look like they could be real photographs. They were invented about ten years ago, in 2014, and were a major development in AI.
How do generative adversarial networks work?
A GAN works by having two AI models compete against each other. One is the generator, which creates the fake data, and the other is the discriminator, which aims to distinguish the fake data from real data. These two models are trained in tandem and improve in an arms race against each other — hence the term “adversarial.” In its quest to fool the discriminator, the generator quickly learns to create data that is very challenging for a human to distinguish from real data.
How are generative adversarial networks used in AI?
GANs can create images, text, or audio that resemble the real thing. For example, GANs can create new paintings in your favorite style. A big topic in AI these days is synthetic data, fake data that can be used to train AI models when you don’t have enough real data. A GAN can create a set of synthetic data that resembles the real data, which can then be used to train another AI model.
Also Read: Explained: Quantum Generative Models
What are some of the GAN variants?
A few different types of GAN specialize in certain tasks. For example, if you wanted to create a model that could generate pictures of cats, you could use a GAN. If you want to generate pictures of dogs, you can use a second GAN. Or you could have a single model capable of doing both if you use a Conditional GAN or cGAN, which can accept a label (“cat” or “dog”) as part of the input and use that when it generates the image. Another example is the CycleGAN, which learns how to change one type of data into another. For instance, a CycleGAN might learn to turn a photograph into a pencil drawing.
