Synthetic Data Generation

What is synthetic data generation?

Synthetic data generation is the process of creating artificial data, such as images, sensor readings, text, or tables, that captures patterns or characteristics of real-world data. In AI, synthetic data is used to train and test models when real-world data is scarce, sensitive, expensive to collect or label, or difficult to obtain safely.

Synthetic data does not necessarily contain records of real people or events. However, data generated from real-world sources can sometimes reproduce sensitive information, depending on the methods used.

How does synthetic data generation work?

Simulation-based synthetic data generation is commonly used for images and sensor data. It can involve several steps:

1. Build the environment: Create a 3D scene or digital twin representing the objects, environment, and sensors relevant to the AI system.
2. Randomize: Vary factors such as lighting, object positions, textures, camera angles, and environmental conditions to increase dataset diversity.
3. Capture: Render images, video, or sensor data such as depth and lidar from the simulated environment.
4. Label automatically: Generate annotations such as bounding boxes, segmentation masks, and object positions directly from the simulation.
5. Validate: Evaluate models trained on synthetic data against real-world data and refine the simulation to address differences between the two.

For example, a manufacturing research team can create a digital twin of a factory and use it to generate labeled training data for AI systems. The Korea Institute of Industrial Technology (KITECH) used Unity Industry to build a digital twin of a metal casting foundry and develop a synthetic data pipeline for manufacturing AI, generating more than 100,000 raw frames for human-robot collaboration AI in a 14-week project.

What are the main synthetic data generation techniques?

Synthetic data can be generated using several approaches, depending on the type of data and the intended use:

Different techniques can also be combined. For example, generative models can create 3D assets or environments that are then used in simulation-based data generation.

What is the difference between synthetic data and real data?

Synthetic data and real-world data have different strengths and limitations, so many projects use a combination of both.

What is synthetic data used for?

Synthetic data is used across industries to train and test AI models, simulate difficult or uncommon scenarios, and develop systems when real-world data is limited, sensitive, or expensive to collect.

• Computer vision: Train object detection, segmentation, and pose estimation models using images with automatically generated annotations.
• Robotics: Train robots to perceive and interact with objects in simulated environments before deployment in the real world, including through robotics simulation.
• Autonomous vehicles: Generate and test uncommon or hazardous driving scenarios that can be difficult or unsafe to capture on real roads.
• Manufacturing: Use factory simulations and digital twins to generate training data for inspection, automation, and human-robot collaboration.
• Software testing: Generate realistic test data without using actual customer records.
• Healthcare and finance: Generate data for testing, research, or model development while reducing exposure to sensitive information, depending on how the synthetic data is generated and validated.

What are the benefits of synthetic data generation?

• Scale on demand: Generate large amounts of data without relying entirely on real-world data collection.
• Automated labeling: Generate annotations or metadata automatically, depending on the generation method, reducing the need for manual labeling.
• Reduced exposure to sensitive data: Synthetic datasets can reduce the need to use or share real personal or sensitive records.
• Edge-case coverage: Generate or simulate uncommon, hazardous, or expensive scenarios that may be difficult to capture in the real world.
• Dataset diversity: Add underrepresented scenarios or conditions to improve dataset coverage when the generated data accurately reflects relevant real-world variation.

What are the challenges of synthetic data generation?

• The sim-to-real gap: Models trained on simulated data can struggle when real-world conditions, such as lighting, textures, physics, or sensor noise, differ from the simulation.
• Validation effort: Teams need to evaluate whether synthetic data adequately represents the conditions the model will encounter, often by testing against real-world data.
• Inherited bias: Synthetic data generated from biased source data or assumptions can reproduce or amplify those biases.
• Privacy isn't automatic: Synthetic data generated from real records can potentially reproduce sensitive information, so appropriate privacy and re-identification testing may be necessary.

Frequently asked questions (FAQ)

Is synthetic data the same as fake data?

Not quite. Synthetic data is artificially generated to reproduce relevant patterns or characteristics of real-world data. It can be designed for specific uses, such as training AI models, testing software, or simulating real-world conditions. Dummy data is generally created for testing or development and does not necessarily aim to reflect real-world patterns.

Can AI models be trained only on synthetic data?

In some cases, yes. Whether a model can be trained effectively using only synthetic data depends on the task, the quality and diversity of the data, and how closely it represents the conditions the model will encounter. Combining synthetic and real-world data can help provide broader coverage while allowing models to be evaluated against real-world conditions.

Is synthetic data always private?

Not automatically. Synthetic data generated from real records can potentially reproduce sensitive information if the generation process memorizes or closely reproduces source examples. Appropriate privacy testing can help identify these risks before data is shared. Simulation-based data generated from virtual environments does not start from personal records, although its privacy properties still depend on how the system and assets are created.