Generative Adversarial Network (Gan)
A Generative Adversarial Network (GAN) is a class of machine learning frameworks designed for generative AI. It was initially developed by Ian Goodfellow and his colleagues in June 2014. A GAN consists of two neural networks, a generator and a discriminator, that compete with each other in a zero-sum game, where one agent’s gain is another agent’s loss.
Areas of application
- Computer Vision
- Natural Language Processing
- Robotics
- Medical Imaging
- Audio Generation
Example
For example, a GAN could be used to generate realistic images of faces. The generator network would produce new face images, while the discriminator network would try to distinguish between real and fake faces. Through this competition, the generator would learn to produce more realistic images over time.