A Gan
A Generative Adversarial Network (GAN) is a type of artificial intelligence (AI) model that consists of two competing neural networks: a generator and a discriminator. The generator’s goal is to create synthetic data samples that are indistinguishable from real data, while the discriminator’s goal is to accurately classify whether a given sample comes from the real or generated distribution.
Areas of application
- Image synthesis
- Deepfake technology
- Medical imaging
- Anomaly detection
- Simulation in physics
- Text generation
- Data augmentation
Example
For example, a GAN could be used to generate realistic images of faces. The generator would take a random noise input and produce an image of a face that looks like it was taken from a real photograph. The discriminator would then try to classify the generated image as either real or fake, providing feedback to the generator to improve its performance.