Basics of Generative Adversarial Networks
— GANs Series Part 1
Introduction to GANs (Generative adversarial networks)
GANs are a type of artificial intelligence framework that consists of two neural networks, a generator, and a discriminator, that are trained simultaneously in a competitive manner. The generator’s role is to create new data samples, such as images or text, that are intended to be indistinguishable from real data samples, while the discriminator’s role is to differentiate between real and generated data samples.
The intuition behind GANs is that by pitting the generator and discriminator against each other in a game-like fashion, they can learn to improve iteratively. The generator learns to generate more realistic data by trying to fool the discriminator, while the discriminator learns to better distinguish between real and fake data samples. As a result, the generator gets better at generating realistic data samples, and the discriminator gets better at detecting generated data.
Generator:
The generator network inputs a random noise vector and generates synthetic data samples, such as images or text. The generator aims to produce data that is indistinguishable from real data.
Discriminator:
The discriminator network receives both real data samples from the training dataset and fake data samples generated by the generator. Its task is to distinguish between real and fake data samples. The discriminator is trained to correctly classify real data as real and fake data as fake.
Conclusion:
The ultimate goal of GANs is to generate data samples that are virtually indistinguishable from real data, in a wide range of applications from generating images to creating realistic voice recordings. The competitive nature of GANs allows them to produce high-quality outputs, making them a popular choice for generating realistic data in various fields of artificial intelligence.
In the coming articles, we will dive deep into GANs architecture and different types of GANs, problems faced and solved by GANs.