Image generation is a process by which an artificial intelligence algorithm creates an original image or visual content without the use of any pre-existing images as reference or input. This is achieved through a deep learning technique called generative adversarial networks (GANs), which consist of two neural networks: a generator and a discriminator.
The generator network is responsible for creating new images by learning patterns and features from a dataset of existing images. It generates new images by manipulating these learned patterns and features, essentially creating something entirely new.
The discriminator network, on the other hand, is responsible for determining whether an image is real or generated by the generator network. It learns what distinguishes real images from generated ones, which helps it better identify fake images created by the generator.
The two networks compete against each other, with the generator trying to create more realistic images while the discriminator tries to distinguish between real images and generated ones with greater accuracy over time, resulting in increasingly more realistic images being generated by the network over time, as it learns how best to fool the discriminator into believing they're real images, rather than generated ones created by itself or another network's generator module.