Synthetic data generation is the process of creating artificial data that mimics real-world data. This technique is often used in machine learning and AI to generate large datasets for training models, especially in situations where real data is scarce, expensive to obtain, or sensitive in nature.
How Synthetic Data Generation Works
To create synthetic data, algorithms often use existing data as a basis to understand patterns and distributions. Then, they generate new data points that are statistically similar but not identical. For example, in image processing, synthetic data might include new images that are variations of existing ones, tweaked in terms of lighting, angles, or background. In finance, it could involve generating transactional data that mirrors real customer behavior without using actual customer data.
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