What Is Synthetic Data?

Enterprises need a holistic, end-to-end strategy for how they’ll develop models and use them to address specific business challenges. Typical datasets used for creating these computer vision models rely on real-world events and experiences. The process is not as simple, however, as turning on cameras and feeding the resulting visual data into a model. 

Use case likelihood – that is, the odds a camera will capture rare but important objects or scenarios to help train the model – adds a host of challenges. Enterprises may require data related to rare or sporadic occurrences like accidents and equipment malfunctions or need to account for differences in environment. With the addition of synthetic data to their datasets, enterprises will be less hindered by these resource limitations and have the opportunity to fill any gaps.

What Is Synthetic Data?

Synthetic data is data that – rather than being captured in the real world – is created and, sometimes, annotated by deep learning algorithms. It’s often gathered with the help of game engine environments, which provide a digital staging ground with the ability to not only generate different types of images, but also modify light and shadow conditions. Organizations can leverage synthetic data as both a supplement and an alternative to data from real-world observation. 

Why Use Synthetic Data? 

Even at its most efficient, the process of gathering and managing “real” data is potentially stymied by factors related to logistics, time, and expenses. Simply collecting imagery can be expensive and time consuming. The subsequent processes of annotating datasets and ensuring their quality present even more challenges. In this context, synthetic data has the potential to save businesses time and money. 

What’s more, data collection presents a number of risks, including those related to safety. In the world of automated vehicles, for instance, synthetic data can help cars learn to recognize pedestrians of various sizes without putting lives at risk.

In short, by leveraging synthetic data, organizations can easily train their models to address uncommon scenarios that might be tough to capture in the real world and save time they might otherwise spend creating these scenarios. Deep learning algorithms can concoct every conceivable event and image, dramatically increasing the number of use cases a model can help to address. If they’re supplementing an existing dataset, enterprises can make a point to include objects and scenarios that are missing. In a matter of hours, enterprises can generate hundreds or thousands of images, capturing the full complexity of their potential use cases without significant additional hard work.

Synthetic datasets are particularly useful in instances where no data or insufficient data is available. One problem that often occurs when models are trained with insufficient data is overfitting. This phenomenon sees neural networks apply too-narrow definitions. An object detector trained to recognize cars, for example, might fail to detect certain makes and models if it has never seen them during training.

Artificially-generated datasets are also a vital resource when datasets are affected by bias. Artificially supplementing a dataset can help fill in any gaps in the types of people, objects, or situations included. What’s more, helps reduce the chances of implicit or explicit bias from human users affecting the composition of the dataset. 

Enterprises are well aware of the potential value of synthetic data and are already making liberal use of it. Gartner predicts that synthetic data will “completely overshadow” data derived from traditional sources by 2030 for use with AI models.

In general, synthetic data provides a digital testing and training ground. Enterprises can experiment, capture imagery of all types, compare different cameras and sensors, and get quick answers to questions without fear of wasted resources

Risks and Challenges of Using Synthetic Data

Synthetic data is a cost effective choice for enterprises, but datasets built algorithmically aren’t without their shortcomings and risks. An artificial dataset is generally only as good as whatever system created it. While enterprises save time by leveraging synthetic datapoints, they’ll still need to verify their results against results achieved with real-world data.  

“Synthetic data adoption is occasionally limited by misconceptions and skepticism,” notes Warren De Wit, a Machine Learning Engineer at Plainsight. “If enterprise stakeholders hear the word synthetic and think ‘fake,’ they may be less likely to embrace this type of data.” Some advocates are already addressing concerns over realism by developing AI solutions specifically for “translating” synthetic data. 

3-D Image Generation and Synthetic Data

Techniques for generating synthetic data vary, but 3-D assets – which range from people and objects to textures and colors – frequently play a role. With the quantity of available data increasing all the time, organizations may not even have to generate their own digital resources; for instance, numerous online marketplaces offer asset packs created by artists for use in video games and other media. Deployed in model testing settings, these can help to fill gaps and ensure as broad and diverse a dataset as possible. Enterprises who take synthetic data generation into their own hands might use a photogrammetry tool or a flatbed scanner to create 3-D facsimiles of real-life objects.  

Make the Most of Synthetic and “Real” Data

Plainsight’s team knows how and when to augment a dataset with synthetic imagery and uses synthetic data regularly in addressing customer challenges. To discuss the ways our end-to-end visual data management can help your enterprise deliver on its goals, schedule a conversation with today.