Fake It To Make It: Using Synthetic Data to Augment Sparse Datasets
Synthetic data promises massive sets of perfectly generated training data for a fraction of the cost of manually sourced annotated data. But there remains doubt about the efficacy of using synthetic data sets to train machine learning amongst practitioners. In this talk, Daeil Kim, a machine learning researcher and founder of AI.Reverie delineates the advantages of synthetic data and how to avoid traps that lead to dead zones and false positives. He also reviews work of simulations for synthetic data in application verticals that are traditionally difficult to manually acquire significant data sets.