Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods
This paper addresses the problem of data scarcity for the machine learning community, particularly for sensitive domains like healthcare, by advocating for synthetic data.
This paper discusses synthetic data as a solution to the limited availability of sensitive large-scale datasets, such as healthcare data, for machine learning research. It aims to accelerate progress by enabling the broader machine learning community to access and utilize meaningful data at scale.
Many ground-breaking advancements in machine learning can be attributed to the availability of a large volume of rich data. Unfortunately, many large-scale datasets are highly sensitive, such as healthcare data, and are not widely available to the machine learning community. Generating synthetic data with privacy guarantees provides one such solution, allowing meaningful research to be carried out "at scale" - by allowing the entirety of the machine learning community to potentially accelerate progress within a given field. In this article, we provide a high-level view of synthetic data: what it means, how we might evaluate it and how we might use it.