Generating Synthetic Data for Improved AI Performance
Machine learning models rely on large amounts of data to accurately identify patterns and make predictions. However, collecting and labeling large amounts of data can be time-consuming and expensive. This is where synthetic data comes in. Synthetic data refers to artificially generated data that is designed to mimic real-world data. In this blog, we will discuss how generating synthetic data can improve machine learning performance. What is Synthetic Data and How is it Generated? Synthetic data is artificially generated data that is designed to replicate real-world data. It is typically created using algorithms that generate data based on statistical patterns found in existing data. This can include data augmentation techniques such as flipping, rotating, or zooming in on images to create new data. Other techniques involve the use of generative models, such as generative adversarial networks (GANs), which generate new data by learning the statistical patterns of existing data. Why is S
