GAN (Generative Adversarial Network) is a type of AI model made up of two neural networks: a generator and a discriminator, which work together in competition. The generator creates new data (like images or text), while the discriminator evaluates how real or fake that data is. Over time, the generator improves at creating realistic outputs.
Example: GANs are used to generate realistic images, such as creating lifelike faces of people who don’t exist, or for tasks like enhancing image resolution or generating art.
GANs are powerful tools for creative and data generation tasks but can also raise concerns, such as their use in creating deepfakes.