What is a GAN (Generative Adversarial Network)?

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How GANs Work:

A GAN consists of two neural networks that compete against each other:

1. Generator

   • Creates fake data (e.g., images, text) from random noise.

   • Its goal is to produce data so realistic that it fools the discriminator.

2. Discriminator

   • Evaluates data and tries to distinguish between real data (from the training set) and fake data (produced by the generator).

   • It outputs a probability indicating whether the input is real or fake.

The “Adversarial” Process:

• The generator tries to improve so it can fool the discriminator.

• The discriminator tries to get better at spotting fakes.

• This creates a zero-sum game where both networks get better over time.

Applications of GANs:

• Generating realistic images or videos (e.g., deepfakes)

• Creating art or music

• Image-to-image translation (e.g., turning sketches into photos)

• Data augmentation for training other models

• Super-resolution (increasing image resolution)

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