Generative Adversarial Networks (GANs) are a class of machine learning frameworks that consist of two neural networks, called the generator and the discriminator, which are trained together in a competitive setting. The generator creates new data samples—such as images, text, or audio—from random noise, while the discriminator evaluates the samples, distinguishing between real data from the training set and the fake data produced by the generator. The goal of the generator is to produce realistic samples that can fool the discriminator, while the discriminator aims to accurately identify real and fake samples. This adversarial process leads to both networks improving over time.
The training process of GANs operates in a loop. Initially, the generator might produce low-quality data that is easily detected by the discriminator. As training progresses, the generator learns from the feedback of the discriminator, iteratively improving the quality of the data it generates. Meanwhile, the discriminator also evolves to become better at recognizing the subtle differences between real and generated data. For example, in image generation tasks, the generator may start by creating blurry images but gradually refines its outputs to produce high-resolution images that resemble those in the training dataset.
GANs have practical applications in various fields. In the art domain, they can generate new artworks by learning from existing styles, resulting in pieces that can mimic famous artists. In the realm of gaming and virtual environments, GANs can create realistic textures and backgrounds. They are also used in medical imaging to enhance image quality, or to create synthetic medical data for training purposes without compromising patient confidentiality. Overall, GANs offer a powerful way to generate new data while learning from existing datasets, making them a valuable tool in artificial intelligence.