While there is no universal approach to the generation of synthetic data, there are a number of different methodologies that can be applied in many different situations. These methodologies differ from each other in their assumptions about data and the process of processing them. However, they do share a similar goal of facilitating research by enabling researchers to use background knowledge to generate synthetic data. Here are three of these methods and how they can be used in different contexts.
Variety
To build an effective machine learning model, diversity and variety are critical. Training data should mimic real-world conditions. For instance, an expense receipt might have a coffee ring or crumpled paper, or may have been taken in low-light. Synthetic data producers need to take this into account, too. Moreover, the more data you have, the better. Synthetic data providers need to take into account the variety of data, including the size of the original dataset.
Diversity
Synthetic data has many potential uses, from reducing AI errors to building full-spectrum training sets. With a diverse array of artificial faces, synthetic data can accurately represent a wide variety of people. The diversity created by synthetic data is also useful in physics research, where a wide variety of real-world data sets can help train radar systems. Among its other applications, synthetic data has potential to improve robotics and general speech management.
Utility
Often, the utility of synthetic data generation is determined by its similarity to the original dataset. For example, the utility of article citation count can be judged by polling the highest number from various sources. A dataset with similar statistical properties is useful if it can be used to infer the relative utility of articles, but its specific utility may be weaker. This article discusses utility as a tool for data exploration. In this article, we will look at utility metrics and its application in a synthetic data generation environment.
Cost
Synthetic data can be produced in massive quantities and can be used to train artificial intelligence. Real-world data is often too expensive and dangerous to use. For this reason, companies working on autonomous vehicles have turned to simulations to create synthetic data. Because synthetic data is known to be ground truth, it doesn’t need to be labeled. Synthetic data also works well for radar and infrared computer vision applications. The cost of synthetic data generation is exponentially less than that of real-world data.
Privacy
The recent General Data Protection Regulation (GDPR) provides legal protection for the user’s personal data but also introduces a number of new technical challenges. Privacy of synthetic data generation is one such solution and could help maintain the speed of development and the ability to innovate. In addition to being a practical privacy-enhancing technology, synthetic data can also help developers build machine learning models for many secondary uses. It can also be used to enhance collaboration between external data scientists and data owners.
Openness
The openness of synthetic data generation is an important issue. Synthetic data is created by taking data from diverse sources and combining it into a single dataset. Although the privacy of individual users’ data is a primary concern, recent research suggests that synthetic data is not as open as previously thought. The reason is that the process of generating synthetic data requires no anonymity, so there is no way to determine who a given piece of data belongs to.
Competitions
The competitions for synthetic data generation have gained considerable momentum in recent years. Participants in these competitions are grouped into teams from universities, biotech companies, and pharmaceutical companies. Each team had five hours to formulate a solution, and five minutes to present it. Teams incorporated both existing and novel technologies, and they developed their algorithms for use in rare disease datasets. The winning team proposed a method that could be used to predict the next major breakthrough in a wide range of diseases.
