Understanding the Architecture of GANs

In 2014, Ian Goodfellow presented a revolutionary method in machine learning called Generative Adversarial Networks (GANs). The two neural networks that make them up, the discriminator and the generator, are trained in tandem using an adversarial competition method. The generator mimics genuine data by producing synthetic data, while the discriminator determines whether the data is real or produced. Until the produced data is almost indistinguishable from the actual data, this adversarial process aids the generator in improving its output.

A random noise vector is used as an input by the generator, which then produces a meaningful output, such as a picture. The discriminator, on the other hand, gets both real data and data that was made up, and it has to tell the difference. The goal is to teach the generator to make fake data that the discriminator can't reliably tell apart from real data. This will make the fake data look a lot like real data. The discriminator is a normal predictor, but its job is different because it helps improve the generator's results while it's being trained.