A Generative Adversarial Network (GAN) is a type of deep learning model designed for generative tasks, particularly in image synthesis, data augmentation, and creative AI applications. GANs consist of two neural networks—the generator and the discriminator—that work in opposition to improve each other iteratively.
Architecture of GANs
GANs are composed of two competing neural networks:
Generator (G):
The generator creates synthetic data that mimics real data distributions.
It takes a random noise vector (often from a Gaussian or uniform distribution) and transforms it into structured data (e.g., an image).
Discriminator (D):
The discriminator is a binary classifier that distinguishes between real and generated data.
It receives both real samples from a dataset and synthetic samples from the generator, and it learns to classify them correctly.
Training Process
The GAN training process is based on a min-max game where both networks improve over time:
The generator tries to fool the discriminator by producing more realistic samples.
The discriminator attempts to identify fake samples accurately.
Over time, the generator produces data that is almost indistinguishable from real data.