DCGANS for CIFAR-10 Dataset. Introduction

Artificial intelligence approach called GANs (Generative Adversarial Networks) is used to create new, synthetic data that is similar to a training dataset. They are made up of a generator and a discriminator neural network. The discriminator seeks to separate the synthetic data from the actual training data, while the generator tries to produce synthetic data comparable to the training data. The two networks are simultaneously trained, and while the generator attempts to provide data that can trick the discriminator, it gets better over time. Numerous types of synthetic data, including images, audio, and text, have been produced using GANs.