The Advantages and Disadvantages of Synthetic Training Data

Synthetic data can supplement image and video-based datasets that otherwise would lack sufficient examples to train a model, improving the performance and accuracy of computer vision models. However, using synthetic data also comes with pros and cons, including solving problems of edge cases and outliers, reducing data bias, saving time and money by augmenting real-world datasets, navigating data privacy regulatory requirements, and increasing scientific collaboration. On the other hand, generating synthetic data can be cost-prohibitive for smaller organizations and startups, remains a tradeoff between achieving differential privacy and accuracy, overtraining risk, verifying the truth of the data produced, and potential biases in the generated data. Despite its challenges, synthetic training has the potential to revolutionize fields where real-world datasets are scarce and accelerate the development of medical computer vision and artificial intelligence models for treating patients in developing nations with limited medical access and resources.

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