Let’s advanced AI
with Synthetic Data
Synthetic data refers to data generated in a digital environment rather than collected in the physical environment. By constructing a situation or environment that cannot exist in reality, it is possible to mass produce new data from various domains. The value of synthetic data is becoming higher because it contributes to the enhancement of accuracy in the training of AI models and enables the development of Advanced AI with less amount of real data.
static data
Static data refers to synthetic data of a static object that can be 3D modeled. Various types of realistic synthetic data can be generated by modeling based on the numerical information of real objects and applying parameters such as Material, Lighting, and Environment.
dynamic data
Dynamic data refers to synthetic data for objects with actions like humans or animals. Accurate action data can be collected with a high-precision motion capture system, and dynamic synthetic data can be created by integrating a static data generation system.
Why do we need synthetic data?
- Diversification of training data for accurate AI model development
- Dataset optimization to meet machine learning performance metrics
- Quantitative and qualitative advancement of training data
- Accurate auto labeling
- Created based on simple rules, statistical modeling, simulation, and various technologies
- Cuts down the time by half and the cost by 1/10 for data labeling tasks
- Addressing sensitive privacy issue
*Source: Gartner, “Maverick Research: Forget About Your Real Data – Synthetic Data Is the Future of AI,” Leinar Ramos, Jitendra Subramanyam, 24 June 2021.
AI research
AI research is being carried out across various fields such as Image classification, Object detection, and Pose estimation. AI models are becoming more advanced day by day, and synthetic data is also gaining popularity as the need for high-quality training data has emerged. Therefore, it will be possible to improve the accuracy of AI models that have already been developed and provide optimal synthetic data for AI models that have limitations in collecting real data, thereby providing more accurate and faster results to AI model developers.
Image classification
Object detection
Pose estimation
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