Synbild

Synbild is a term used in discussions of image synthesis and visual perception to denote synthetic visual representations produced by computational processes. It encompasses still images and sequences that simulate real-world scenes, fictional settings, or abstract patterns. The term is primarily used in computer vision, cognitive science, digital art, and media studies.

Etymology: "synbild" appears as a portmanteau of synthesis or synthetic and image; the exact origin is informal and varies by discipline. It is sometimes written as "synbild" or "syn-bild" in literature.

Generation and methods: Synbilds are produced through procedural generation, rendering, or learned generative models such as GANs and diffusion models. They can be photorealistic, stylized, or schematic. Data synthesis may involve domain-specific constraints, such as lighting, texture, or anatomy for medical images.

Applications: In machine learning, synbilds are used for data augmentation, like expanding datasets to improve object detection or segmentation. They are used in simulations for robotics and autonomous vehicles, virtual reality content creation, and in studies of perception.

Advantages and challenges: They offer controlled, scalable data creation, rapid iteration, and access to rare cases. Drawbacks include potential domain gap between synthetic and real data, risk of overfitting to artifacts, and concerns about provenance and bias.

See also: synthetic data, generative model, diffusion model, data augmentation, computer graphics.