Synthetic Data Will Revolutionize Machine Learning Model Training
As a company that was founded and funded on the merits of synthetic data generation, we thought it only proper to dedicate a post to the what, why and how of synthetic data.
Synthetic data has made small, incremental steps in the last two decades or so from academia and into the commercial world. Only in the last few years it started to gain momentum, specifically due to the need for massive amounts of training data for machine learning / neural networks / AI algorithms. But the future looks bright for synthetic data; according to Gartner, by 2022 25% of training data for AI will be synthetically generated.
This nicely frames our discussion. Although synthetic data can have various use cases, we’ll focus on synthetic data for machine learning, and even more specifically, synthetic training data.
We’ll provide the necessary background information on synthetic data, explain the problems it solves, the benefits it brings and try to back up the big, declarative title of this post.
What is Synthetic Data
Simply put, synthetic data is data generated, not collected. As opposed to real data that records actions, happenings, or environments, synthetic data is created, from scratch, to either replace or complement real data.
The most common way in recent years to explain synthetic data is by the following example, taken from the autonomous vehicle universe.
Autonomous vehicles need to ‘learn’, among other things, how to avoid hitting pedestrians that abruptly enter the road. Autonomous vehicles learn by way of examples; here are a million images of stops signs from every possible angle, time of day, weather conditions etc. – now the vehicle ‘knows’ to recognize a stop sign no matter what.
But how do you find images of pedestrians jumping into the road? You don’t. So create them. You generate artificial images, a.k.a synthetic data, of the jumping-to-road scenario to help the autonomous vehicles learn how to avoid hitting pedestrians.
This right there is a classic model training.
The autonomous vehicles market is far from being the only market that takes advantage of the availability and benefits of synthetic data. Other markets are retail, smart cities, medical equipment, heavy industries, and the AR/VR universe.
The Problems Synthetic Data Solves
Lack of Real Data
The first and most obvious problem synthetic data solves is a lack of real data. As explained above in regards to autonomous vehicles, sometimes you just don’t have enough real data for machine learning algorithms. The only way to fill this gap is by generating synthetic data for training.
Real Data is Expensive
It is. Looking at our domain of expertise, remote sensing imagery (satellite, aerial, drone), acquiring such images is a costly endeavor, especially considering the tens of thousands if not hundreds of thousands of images needed for machine learning image analytics. Synthetic data can be produced for a fraction of the cost of real data.
Data Preparation for Machine Learning Training Is a Serious Bottleneck
There’s a lot to be said about this, but we’ll try to be brief. Data, real or synthetic, cannot just be handed to the machines – it needs to be prepared for training. But real data needs to be prepared manually, yes, by actual humans. The bottleneck that is formed is easy to comprehend – there’s no way manual labor can ‘catch up’ with the speed that machine learning can absorb data. Synthetic data is generated ready-for-training, thus has the potential to release said bottleneck.
How Do You Generate Synthetic Data
That’s the real fun part. Since synthetic data is generated from scratch, there are basically no limitations to what can be created; it’s like drawing on a white canvas.
We can’t speak for everyone, but we, at OneView, use gaming engines to generate our synthetic data that replaces remote sensing imagery; the same engines used for titles like GTA and Fortnite. The creation process is done in 3D to allow complete control of every element in the environment and the objects populating it. You can learn more about our 6-layer process here.
Overall, synthetic data generation is fast, and the data generated is fully-prepared for machine learning training. Additionally, you can generate an endless variety of the subject matter, as mentioned earlier, crucial for providing comprehensive training material.
Another important thing to understand about synthetic data generation is this: the more you invest in it, the better the results you’ll get in algorithm training. We invest a lot in appearance and randomization, two elements we found have a very positive impact on training results. The closer synthetic data resembles real data – with all its imperfections! – and offers a wide variety of structures, environments, scenarios (the result of detailed object modeling) and inherent randomized nature, the better the learning process will be. We have plenty of data to support this claim, but that’s a topic for a different post.
Benefits of Synthetic Data
There are many. Let’s divide it into generation and usage.
Benefits of Synthetic Data Generation
- No limitations – you have the ability to create any environment, place in it any object, and adapt it to any time of day or night, weather condition
- It’s a fast process
- It’s scalable – an almost endless amount of synthetic data can be generated
- It is fully customizable to answer any needs and requirements
- It is cost-effective – much more effective than real data
- It is highly accurate since every pixel is controlled
- The ‘data preparation’ is a part of the creation process
Benefits of Using Synthetic Data for Machine Learning Training
- No learning curve – not for you or for the machines
- It accelerates your operation – your machine learning training is no longer bottlenecked
- It allows you to ‘skip’ the manual process of data preparation
- It allows you complete control over the content of the data – you can ‘order’ the exact use case you are training your machines for
- It enables your operation to quickly adapt to changing circumstances
- It saves you costs
To Sum Up (and Back Up the Big, Declarative Title)
Taking together all the benefits of synthetic data, as far as generation and usage, there’s no escaping the conclusion that synthetic data indeed has the potential to revolutionize machine learning model training. And it will.
The fact that until now machines needed to rely on human manual work for their training is ‘a glitch in the system’. As always, technology has caught up and now data can be synthetically generated for training purposes, with the required speed and scale to support the evolving needs of data analytics.
The fact that synthetic data releases the bottleneck that’s holding back AI is significant. Combine that with the limitless flexibility of synthetic data to generate any use case with any specifications to answer and requirements and you’ve got a truly revelatory leap forward.