A GAN, or Generative Adversarial Network, is a type of artificial intelligence model that can create new data resembling examples it has learned from. It uses two neural networks, called the generator and discriminator, that work against each other during training. The generator creates new content, while the discriminator evaluates whether the content appears real or generated.
Through repeated training, the generator improves its ability to produce realistic results, while the discriminator becomes better at identifying generated content. GANs have been used to create images, enhance photographs, generate realistic faces, produce artwork, and transform existing visual content. They are particularly useful for applications where AI needs to generate data that closely resembles patterns found in real-world examples.
