What Generative Adversarial Network (GAN) Meaning, Applications & Example
A framework for generating new, synthetic data.
What is a Generative Adversarial Network (GAN)?
A Generative Adversarial Network (GAN) is a type of deep learning model consisting of two neural networks—the generator and the discriminator—that work against each other in a competitive process. The generator creates new, synthetic data samples, while the discriminator evaluates them, trying to distinguish between real and fake data. This adversarial process continues until the generator produces highly realistic data.
Components of GANs
- Generator: Creates synthetic data by learning the patterns in the real data. It tries to “fool” the discriminator into accepting generated samples as real.
- Discriminator: Evaluates the authenticity of data, distinguishing between real and generated samples. It provides feedback to the generator, helping it improve over time.
- Adversarial Training: The generator and discriminator engage in a zero-sum game, where the generator aims to maximize its success in generating realistic data, and the discriminator strives to become better at detecting fake samples.
Applications of GANs
- Image Generation: Creates realistic images, used in applications like art, gaming, and media content.
- Data Augmentation : Generates synthetic data in fields like healthcare, where labeled data may be limited, improving model performance.
- Text-to-Image Synthesis: Converts textual descriptions into images, used in creating visual representations from language-based input.
Example of a GAN
An example of a GAN is in artificial image synthesis, where GANs generate realistic human faces by learning from thousands of photos. The model can create new, unique faces that look authentic but are entirely computer-generated, useful in fields like film, gaming, and virtual simulations.