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Synthetic Data Generation

Synthetic Data Generation

Synthetic data generation is a process of generating large amounts of data with strong privacy guarantees, without degrading accuracy. The process takes time, but there are several types of synthetic data. Text-based synthetic data include natural language, tabular, and time-series data. Other categories include audio, video, and simulation. This article will describe the four different types of synthetic data and discuss the benefits and limitations of each. In addition to text-based data, synthetic data can also be generated using images, sounds, or videos.

Multiple imputation methods

Various utility measures have been proposed to assess the quality of imputation of synthetic data. While visual inspection is the most straightforward way to evaluate data quality, it becomes tedious to visualize multivariate relations when more than two dimensions are involved. However, one way to gain a sense of the quality of a synthesis is to predict whether the simulated data will be observed. Among other measures, Snoke et al. have proposed a measure of distributional similarity that is similar to a propensity score.

The difficulty of multiple imputation is that the imputed values may not be true, introducing errors in the simulated data. In the past, the risk of building the wrong model has prompted caution in the application of this method. But, new methods have been developed to address these issues. If you’re planning to use synthetic data to test the accuracy of your model, it’s important to understand how multiple imputation works.

Neural networks

While the first step to generating synthetic data is to gather real-world data. This type of data is much easier to manipulate and can be easily manipulated to make it more accurate. However, some drawbacks to synthetic data are worth mentioning. Several common errors should be avoided to ensure optimal results. These include large amounts of duplicated records, large number of highly unique fields, and long ID fields. For example, if a training set contains many duplicated records, the model will struggle to learn a pattern in the data. Also, a large number of training records may contain private information, such as credit card numbers and bank accounts.

Another challenge to synthetic data generation is the cost and availability of real-world data. Collecting real-world data is costly and time-consuming and often cannot be used for training and testing. In addition, real-world data is too sensitive and limited in availability. Using synthetic data allows researchers to get the same results without compromising privacy concerns. These are just some of the benefits of using neural networks to create synthetic data.


When generating synthetic data, VAEs are often used as a substitute for regular AEs. They allow users to create images that are similar to actual data from datasets. For example, if they have data of a standard 28 x 28 pixel grid, they would like to use a method that produces artificial images that look like the actual images. Regular AEs, on the other hand, produce 784-pixel values that look nothing like digits.

For example, automakers use synthetic data to train self-driving cars and other autonomous vehicles. Robots and drones also use it. This is because it enables these vehicles to behave in ways that humans wouldn’t. This is a particularly important application for industries with limited data usage or privacy policies. By using VAEs in synthetic data generation, they can test fraud detection methods and learn about customer behavior. The benefits of synthetic data are many.

Gretel’s Synthetic Quality Score

The new synthetic report from Gretel features a high-level synthetic quality score along with metrics that help you judge the synthetic data’s utility. The system is tech and vertical agnostic, compatible with a variety of frameworks, apps, and programming languages. Its features also cover data labeling through an API, generating reports and metrics for high-level synthetic quality. The results of these evaluations are immediately available and can be used to improve the quality of synthetic data.

When analyzing real-world datasets, Gretel compares deep structure stability using a principal component analysis, or PCA, to the synthetic data. The system looks for similar size and distribution of elements across all data sets. This is also a useful tool for data science. The results show how Gretel’s approach can be applied to various datasets. In addition to the quality score, Gretel’s synthetic data quality report is accompanied by an analysis of the synthetic data generation process.


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