Introduction

Have you ever tried to get hold of some data for your problem, be it a machine learning problem or some other development-related problem, and you just couldn’t find enough data? Either the data is closed-source and unavailable to you, or it is prohibitively costly or time-consuming to acquire. How do we deal with such a situation?

Well, one solution is synthetic data. Synthetic data is generated by a model to be used in place of real data or with real data. Here, by model, we don’t mean only machine learning or deep learning models; they can be simple mathematical or statistical models too, like a set of (stochastic) differential equations modeling a physical or economic system. Feeling excited yet? Let’s dive more into the details of synthetic data: what it is, how it is generated, and its benefits. You might be able to answer the last one a little by now ;)

What is synthetic data?

As Royal Society defines, synthetic data is the data generated using a purpose-built mathematical model or algorithm to solve a (set of) data science task(s). Keep in mind that synthetic data only mimics the real data and is not generated by real events. Ideally, the synthetic data should have the same statistical properties as the real data it is supplementing. It has many uses, such as improving AI models, protecting sensitive data, and mitigating bias.

Why would you use synthetic data?

Before answering this question, let’s talk a little bit about why real data is not sufficient anymore. Some of the non-exhaustive problems with real data are:

Most of the above-mentioned problems can potentially be solved by synthetic data:

How to generate synthetic data?

Here, we mention some of the ways to generate synthetic data:

In this unit, we will introduce the following methods to generate synthetic data: physically-based rendering, point clouds, and GANs.

Challenges with synthetic data

Now that we have seen the power and uses of synthetic data, let’s take some time out to discuss its challenges:

Resources