GAN, or Generative Adversarial Network, is like having two artists who are working together to create the best piece of art. One artist tries to create the most realistic artwork possible, while the other artist acts as a critic, trying to determine if the artwork is real or fake.
Imagine you have two friends: one who loves to draw and another who is a tough art critic. The artist’s job is to create drawings that look like real-life scenes, while the critic’s job is to look at the drawings and decide if they look authentic or not. The artist and the critic keep challenging each other—over time, the artist’s drawings become better because they learn what makes the drawings look more realistic, and the critic gets better at spotting flaws.
In the world of computers, GANs work in a similar way. They use two neural networks: one called the “generator,” which creates new data (like images), and another called the “discriminator,” which evaluates how realistic the data is. The generator tries to produce data that looks real, and the discriminator tries to tell whether it’s fake or genuine. Through this back-and-forth process, the generator gets better at creating realistic data over time.
For example, a GAN can be used to create realistic-looking photos of people who don’t actually exist. The generator creates these images, and the discriminator checks them to see if they look like real photos. As the GAN trains, the generated images become more convincing.
In simple terms, a GAN is a system where one part tries to create something realistic and the other part tries to judge how realistic it is. This ongoing challenge helps the system improve and produce more realistic results.
