How does a GAN (Generative Adversarial Network) work?

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A Generative Adversarial Network (GAN) is a type of neural network architecture used for generating new, synthetic data that resembles real data. It consists of two neural networks — the Generator and the Discriminator — that compete against each other in a game-like setup.

How GANs Work

1. Generator (G)

   • Takes random noise as input and tries to generate realistic data (e.g., fake images).

   • Its goal is to fool the Discriminator into thinking the fake data is real.

2. Discriminator (D)

   • Receives both real data (from the dataset) and fake data (from the Generator).

   • Its job is to distinguish between real and fake data.

   • Outputs a probability: real (close to 1) or fake (close to 0).

Training Process

1. The Generator creates fake data.

2. The Discriminator evaluates both real and fake data.

3. The Discriminator is trained to improve its classification.

4. The Generator is trained to improve its ability to fool the Discriminator.

5. This process repeats in a loop, improving both models over time.

Objective

• The Generator tries to maximize the Discriminator's error.

• The Discriminator tries to minimize its own error.

• This creates a minimax game where both networks learn from each other.

End Goal

Training continues until the Generator produces data so realistic that the Discriminator can no longer reliably tell real from fake (i.e., ~50% accuracy).

In summary, a GAN works by having two networks compete: the Generator creates fake data, and the Discriminator tries to detect it. Over time, this adversarial process helps the Generator produce highly realistic data.

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