Architecture
Generative Adversarial Network (GAN)
Two neural networks competing to generate realistic data
What is Generative Adversarial Network (GAN)?
GANs consist of two neural networks—a generator and a discriminator—that compete against each other. The generator creates fake data trying to fool the discriminator, while the discriminator learns to distinguish real from fake data. This adversarial process results in highly realistic generated content.
Key Points
1
Two competing networks
2
Generator creates, discriminator judges
3
Produces realistic synthetic data
4
Used for images, video, audio
Practical Examples
Image generation (StyleGAN)
Deepfakes
Art generation
Data augmentation