Synthetic Data Generation: Applications And Challenges For Data Scientists
With the field of technology advancing at the pace it has been over the past decade or so. There’s no surprise that developers are on the lookout for tools and resources that could make the transition easier. While also providing a wide array of benefits to the users of this technology.
One of these resources is synthetic data, which is not only cheaper to produce. Also, supports Artificial intelligence/deep learning by providing them with an abundance of data to build their foundations upon. Synthetic data generation in general helps companies to build software without really having to expose user datasets to developers or external software tools.
What Is Synthetic Data?
Synthetic data is as the name suggests, “artificially” created rather than being generated by actual events. This kind of data is often there with the help of algorithms that chart out data sets. They are useable for a wide array of activities. Such as test data for new products and tools, to play around with model validation. Also in AI model training.
Synthetic data falls under the umbrella of ‘data augmentation’. This refers to the usage of techniques to increase the amount of data available by adding slightly modified copies of already existing data. Or in this case, creating synthetic data from already available data.
Importance Of Synthetic Data
Synthetic data holds importance simply because it can be generated to meet particular needs or conditions that may not be available in the already existing ‘real’ data. What this means is that in cases where a business might be looking for data that meets particular requirements/specifications. This synthetic data is available to cater to those particular needs.
Here are a few cases where this data can be utilized:
1) In instances where privacy requirements might limit the availability of data or how it can be useful.
2) When specific data needs to test a product before its release. But the required data does either not exist or is simply not available to the testers.
3) Particular training data is required for machine learning algorithms. In the case of creating self-driving cars. For instance, the data might be way too expensive to generate in real life and therefore synthetic data takes care of the issue.
What Is A Synthetic Dataset?
As we now know, these datasets are generated through computer programs rather than the documentation of real-world events. The primary aim is to create datasets that are versatile and robust enough to be useful in the training of machine learning models i.e. to ensure that the computing systems learn exactly the kind of information that the user wants it to work with.
Synthetic Data Vs Real Data
The argument always revolves around the question of whether or not synthetic data is better than real data?
An example that we can use to provide support to the argument that synthetic data is better could be the “car-crash” example.
If you’re looking to train AI to avoid car crashes (in the case of self-driving cars). You need training data on car crashes. If you were looking for real data then you’d have to go down a long, expensive, and rather risky road of collecting such data. On the other hand, you could just simulate car crashes and use the synthetic data to train your model!
Now some might say that this example is too extreme. So, we’ll look at a few other points that help explain why synthetic data is preferable to real data.
Real Data Can Be Rare
This follows the argument laid out by the car crash example. Whereby the data is just so rare and hard to get that it’s just better to simulate it and garner the required data accordingly.
Some of the most beneficial uses of AI in fact focus on ‘rare’ events. Since these are ‘rare’ they’re obviously harder to come by. This is where synthetic data can jump in to generate rare events in sufficient quantity to train an AI model.
Synthetic Data Is User-Controllable
Event frequency, object distribution, and repetitions are just some of the aspects of synthetic data that user controls and configure to suit individual requirements. Since everything is controllable and modify accordingly. You have the liberty to quite literally create a near-perfect dataset for your use.
Perfectly Annotated Data
In the case of synthetic data, you can automatically generate a variety of annotations. While this may not sound like a big breakthrough. It is in fact one of the reasons why such data is so cheap as compared to real data.
The main cost of synthetic data is the investment you put into building the simulation. Once that’s done, you just let it generate data in a much more cost-effective manner.
When it comes to non-visible data such as infrared or radar computer vision applications. Synthetic plays a huge role in annotating any form of data for which humans can’t fully interpret the imagery.
Synthetic Data Generation
When creating synthetic data, the obvious first step is to consider the type of synthetic data that you’re aiming for. You have broadly two categories to choose from:
Fully synthetic — this is data that holds no original data. Any reidentification of single units is near impossible and all variables are open.
Partially synthetic — here only sensitive data replace with synthetic data.
Once you decide which category you’d like to proceed with. The next step is to start building the synthetic data using the following strategies:
- Drawing numbers from a distribution: here you will observe real statistical distributions and then reproduce fake data based on them. This is also when you have the option to create generative models and let them randomly generate data going forward.
- Agent-based modeling: in this instance, a model is created first and foremost that helps to explain observed behavior and then moves on to reproducing random data using that very same model. The emphasis here is on understanding the effects of interactions between agents on a system as a whole.