Synthetic data generation methods
There are different methods to generate synthetic data: some of them are based on statistical models and others rely on game engines and simulators. Statistical models are non-deterministic mathematical models that include variables represented as probability distributions. Based on the problem, these models are usually trained using real data to understand the hidden patterns and correlations in the data. Then, the trained ML model can be used to generate new samples automatically, such as images, text, tables, and more. These new samples can be utilized by other ML models for training or testing purposes.
Synthetic data can also be generated using game engines and simulators. These tools are utilized to create 3D virtual worlds. These 3D worlds can be generated using Procedural Content Generation (PCG) techniques to control scene attributes, the interaction between scene elements, and the diversity and quality of the generated data.
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