Generative Adversarial Networks (GANs) were introduced in 2014 by Ian Goodfellow and his colleagues. A GAN is a type of artificial intelligence framework designed for generating new data samples that mimic a given dataset. GANs differ from traditional neural networks, which classify based on the knowledge learned during the training process.
GANs have found applications in various fields, including image and video generation, music synthesis, text creation, and more.
GANs consists of two neural networks: a generator and a discriminator.
- Generator: This neural net creates new data samples. It takes random noise as input and transforms it into data that resembles the training data. The goal of the generator is to produce data that is indistinguishable from real data.
- Discriminator: This neural net evaluates the data produced by the generator and attempts to distinguish it from real data. It is trained to identify whether a given sample is from the real dataset or generated by the generator.
The two networks are trained simultaneously in a process where the generator’s aim is to create increasingly realistic data to fool the discriminator while the discriminator’s aim is to become better at identifying real versus generated data. This adversarial process pushes both networks to…
