Intermediate · Generative AI
Generator
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. The component of a Generative Adversarial Network (GAN) responsible for creating new, synthetic data samples.
Technical Definition
The component of a Generative Adversarial Network (GAN) responsible for creating new, synthetic data samples.
How it works
In the context of Generative Adversarial Networks (GANs), the generator is a neural network that tries to produce realistic data samples from random noise. It works in tandem with a discriminator network, which attempts to distinguish between real data and the fake data created by the generator. Through this adversarial process, the generator learns to create increasingly convincing outputs.
Related Concepts
- Neural Network — A computing system inspired by biological neural networks that learns patterns from data through interconnected layers of nodes.
- Generative Adversarial Network (GAN) — Two neural networks — a generator and discriminator — compete against each other to produce increasingly realistic synthetic data.
- Synthetic Data — Artificially generated data mimicking real-world properties, used for training augmentation or privacy protection.
- Discriminative model — A model that learns to distinguish between different classes or predict a value based on input features.