👉 Computing in paintings refers to the intersection of artificial intelligence, particularly deep learning and generative models, with visual arts. It involves using algorithms to create, analyze, and manipulate images in ways that mimic or even surpass human artistic capabilities. These computational techniques, often based on neural networks like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), enable the generation of new images, enhancement of existing ones, and even the creation of entirely novel artistic styles. By training on vast datasets of existing artworks, these models can learn complex patterns and structures, allowing them to produce paintings that exhibit unique aesthetics, styles, or even entirely new forms of expression. This field not only expands the boundaries of digital art but also provides insights into the creative processes and principles underlying artistic creation.
