Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) are a class of machine learning frameworks designed to generate new, synthetic data that is similar to a given set of real data.

Introduced by Ian Goodfellow and his colleagues in 2014, GANs have become a cornerstone of generative modeling.

Here’s an in-depth look at how they work, their applications, and the challenges they present.

How GANs Work
GANs consist of two neural networks: a generator and a discriminator, which are trained simultaneously but with opposing goals:

Generator (G):

Purpose: To create synthetic data that mimics the real data.
Process: Takes random noise as input and transforms it into data samples.
Discriminator (D):

Purpose: To distinguish between real data and the synthetic data produced by the generator.
Process: Takes both real and generated data as input and outputs a probability indicating whether the input data is real or fake.

The training process involves a two-player minimax game:

The generator aims to maximize the probability of the discriminator being mistaken, i.e., generating data that the discriminator classifies as real.
The discriminator aims to minimize this probability, i.e., correctly distinguishing between real and fake data.