Generative AI

Generative AI refers to artificial intelligence systems that create new content rather than just analyzing or classifying existing data. These models learn the underlying patterns and distribution of their training data, then generate novel outputs that follow those same patterns. In the visual domain, generative AI produces images, videos, 3D models, and design assets that look realistic or match specified criteria.

The main generative architectures for images are diffusion models (Stable Diffusion, DALL-E, Midjourney), which iteratively refine random noise into coherent images, and GANs (Generative Adversarial Networks like StyleGAN), which use competing generator and discriminator networks. Text-to-image models accept natural language prompts and produce matching visuals, while ControlNet adds spatial conditioning through edge maps, depth maps, or pose skeletons for precise output control.

For computer vision practitioners, generative AI is a practical tool beyond creative applications. It generates synthetic training data for rare scenarios (unusual defect types, edge cases in autonomous driving), performs data augmentation (creating labeled variations of existing samples), enables domain adaptation (translating images between visual styles), handles image inpainting and restoration, and powers super-resolution for enhancing low-quality inputs before running detection or segmentation.