Synthetic data, the creation of data similar to real data but tailored to specific requirements, has become an increasingly popular method of generating data for machine learning models. Synthetic data can be useful for organizations lacking a significant amount of real data or with imbalanced datasets. Techniques for generating synthetic data include statistical methods and deep learning techniques like Generative Adversarial Networks (GANs). However, synthetic data can have limitations such as a lack of diversity and biases. Despite this, ongoing research in synthetic data could aid breakthrough use cases and help solve cold start problems as companies adapt to a data-centric approach to artificial intelligence.
source update: A Beginners Guide to Synthetic Data – Towards AI
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