Generative Adversarial Networks (GANs)
Interactively explore how GANs learn to generate realistic data by pitting a Generator against a Discriminator in a zero-sum game.
GAN Training Simulation
Educational GAN Sandbox
Explanation
- Generator: Neural net that creates fake samples from random noise.
- Discriminator: Neural net that tries to distinguish real samples from fake ones.
- Adversarial Training: Both networks improve by competing. The generator tries to fool the discriminator, while the discriminator tries to get better at telling real from fake.
- Loss Functions: GANs use minimax loss:
minG maxD V(D,G)
Click Train Step to see the generator distribution move toward the real data.
Click Reset to restart.
Click Reset to restart.