Artificial Intelligence (AI) creates portraits through various techniques, such as Generative Adversarial Networks (GANs) and Deep Learning algorithms. GANs consist of two neural networks: a generator and a discriminator.
The generator creates an image, while the discriminator evaluates whether the generated image is similar to a set of real images, providing feedback to the generator to adjust and improve.
This process continues until the generator produces an image that is indistinguishable from real images. Deep Learning algorithms, on the other hand, use a convolutional neural network (CNN) trained on a large dataset of images to generate new images.
The AI system uses the learned features from the training images to generate new portraits that are similar in style and content to the training data. However, AI-generated portraits are not perfect, as they are limited by the quality and diversity of the training data, and may also generate unrealistic or disturbing images.