An Introduction to Synthetic Training Data

Despite the vast amounts of data generated daily, machine learning engineers often struggle to source enough high-quality data to train their models effectively. This is particularly challenging in fields where edge cases are prevalent, such as autonomous vehicles and medical imaging. Synthetic training data offers a solution by artificially generating images, videos, and datasets that can significantly increase the size of difficult-to-find datasets. Two methods for creating synthetic data include using game engines like Unity and Unreal to build virtual environments or leveraging deep learning techniques like GANs (Generative Adversarial Networks) to generate artificial data from real-world datasets. While synthetic data has its benefits, it also comes with challenges that need to be addressed for optimal model training.