AiVibeSynthetic Data
Synthetic data is artificially generated data, created by simulation, rendering, rules or generative models, that mimics the properties of real data and is used to train, test or validate AI systems when real data is scarce, costly or sensitive.
Methods include 3D rendering of parts and scenes with varied lighting, poses and textures; physics simulation of sensors and robots; procedural generation of documents or time series; and generative models such as diffusion models and generative adversarial networks. Rendered data comes with exact labels, such as masks and poses, at no annotation cost. For tabular data, generative models can produce records with statistics similar to sensitive originals.
Manufacturers use synthetic images to train inspection and picking models for rare defects or for new products before real samples exist, robot developers train policies in simulation, and organisations use synthetic records to test systems or share data without exposing personal information.
Models trained only on synthetic data can fail on real data because of the domain gap, so synthetic data is usually mixed with real data and always validated on real test sets. Synthetic records derived from personal data can still leak information if the generator memorises examples, so privacy must be assessed.
Key points
- Artificially generated data that mimics real data
- Produced by rendering, simulation or generative models
- Rendered data comes with exact labels at no annotation cost
- Must always be validated against real test data
Where AiVibe comes in
AiVibe delivers AI and machine learning services, chatbots and virtual assistants with RAG, MCP tools and voice, AI quality management including bias detection and model validation, and the AIMURUGA AI agent, and builds Intel-based edge AI devices using the Intel Distribution of OpenVINO toolkit.