GAN Training Visualizer

What is a GAN?

Generative Adversarial Networks (GANs) consist of two neural networks competing against each other: the Generator tries to create fake data, while the Discriminator tries to distinguish real from fake. Through this competition, the Generator learns to create increasingly realistic data.

Key concepts:

• Generator: Creates fake data from random noise, trying to fool the discriminator
• Discriminator: Classifies data as real or fake, trying to catch the generator
• Adversarial Training: Both networks improve by competing against each other
• Nash Equilibrium: The ideal state where neither can improve further
• Mode Collapse: When the generator produces limited variety of outputs