What is Synthetic Data?

Learn about the concept of synthetic data or computer-generated data, and all the practical applications it has with its benefits, drawbacks and potential.

SwissCognitive Guest Blogger: Guda Vineeth Redd and Professor Rajesh Kumar, Woxsen University, India

Synthetic data is data that is generated artificially, rather than being collected from real-world sources. It is often used for testing or training machine learning models, as a way to generate large amounts of data that is representative of the types of data the model will encounter in the real world, but without the need to collect and label real-world data. Synthetic data can also be used to protect the privacy of individuals or organizations, by generating data that resembles actual data but doesn’t include any private information.

What are the methods to generate synthetic data?

Variational autoencoders (VAEs) are a class of generative models that may be applied to produce fictitious data that resembles a training dataset. The way VAEs operate is by first learning a condensed latent representation of the data, which is then used to produce artificial data points.
Generative adversarial networks (GANs) are another kind of generative model that can be used to produce fake data. A generator network and a discriminator network are the two neural networks that make up GANs. While the discriminator network works to separate fake data points from real ones, the generator network creates artificial data points. The generator network tries to create artificial data that can’t be distinguished from actual data, and the discriminator network tries to accurately determine which data points are real and which are artificial. The two networks are trained together.
Neural Radiance Fields (NeRFs) are a type of neural network that can produce artificial 3D scenes. NeRFs work by learning a compact representation of a 3D scene and then using this representation to generate synthetic images of the scene from different viewpoints.

Why synthetic data is important?

Synthetic data can be important for a number of reasons:

Large amounts of data are often required to train machine learning models, and synthetic data can be a convenient and cost-effective way to generate this data.
Synthetic data can be used to test machine learning models in controlled settings, allowing developers to understand how the models will perform under different conditions.
Synthetic data can be used to protect the privacy of individuals or organizations, by generating information that resembles actual data but does not include any sensitive details.
Synthetic data can be used to train machine learning models in domains where real-world data is scarce or difficult to collect, such as in medical imaging or self-driving cars.
Synthetic data can be used to augment real-world data, by generating additional data points These can be used to enhance the performance of machine learning models that are comparable to the real data.

How do data scientists use synthetic Data?

There are several ways in which data scientists can use synthetic data:

Training machine learning models: Synthetic data can be used to train machine learning models when real-world data is scarce or difficult to collect.
Testing machine learning models: Synthetic data can be used to test machine learning models in controlled settings, allowing data scientists to understand how the models will perform under different conditions.
Augmenting real-world data: Synthetic data can be used to augment real-world data, by generating additional data points. These can be used to enhance the performance of machine learning models that are comparable to the real data.
Protecting privacy: Synthetic data can be used to protect the privacy of individuals or organizations, by generating data that resembles genuine data but does not include any private information.
Exploratory data analysis: Synthetic data can be used to explore and understand the characteristics of different data distributions, without having to gather and examine actual data.

In which area the synthetic data is most important?

Using computer-generated data can be particularly important in domains where real-world data is scarce or difficult to collect, such as:

Medical imaging: Synthetic data can be used to train machine learning models for tasks such as detecting abnormalities in medical images, without the need to collect and label large amounts of real-world data.
Self-driving cars: It can be used to train machine learning models for tasks such as object detection and motion prediction, without the need to collect and label large amounts of real-world data from self-driving car sensors.
Robotics: Synthetic data can be utilized to train machine learning models for object manipulation and grasping, without the need to collect and label large amounts of real-world data from robotic sensors.
Industrial applications: To train machine learning models for tasks like quality control and predictive maintenance, without the need to collect and label large amounts of real-world data from industrial sensors.
Military applications: For tasks such as target recognition and situational awareness, without the need to collect and label large amounts of real-world data from military sensors.

Pros and cons of synthetic data:

Some pros of using synthetic data are:

It can be generated in large quantities, making it convenient for training machine learning models that require a lot of data.
It can be generated in a controlled way, allowing data scientists to understand how machine learning models will perform under different conditions.
It can protect the privacy of individuals or organizations, by creating data that resembles genuine data but excludes any private information.
It can be used to train machine learning models in domains where real-world data is scarce or difficult to collect.

Some cons of using synthetic data are:

It may not be an exact match for real-world data, which can affect the performance of When used to solve real-world issues, machine learning models trained on synthetic data are ineffective.
It may not capture the complexity and variability of real-world data, which can affect the generalizability of machine learning models trained on synthetic data.
Generating synthetic data can be time-consuming and require specialized skills, such as the ability to use generative models or expert knowledge of the domain.
It may not be available for all domains or applications, in which case real-world data may be the only option for training machine learning models.

About the Authors:

Guda Vineeth Redd is a post graduate student at Woxsen University pursuing his Masters in Business Administration (business Analytics) and has graduated from JNTUA-Oil Technological and Pharmaceutical Research Institute with a Bachelor of Pharmacy degree (CGPA 7.51).
Vineeth Reddy’s specific areas of interest are in Artificial Intelligence, Machine Learning, Business Analytics, Programing languages.

Professor Rajesh Kumar is a Data Scientist and Assistant Professor at the School of Business at Woxsen University. With a Masters degree under his belt, Rajesh has specialized knowledge in AI Modeling – ML/DL applications as well as IoT and Robotics. As such, he is considered an expert in this field, with hands-on experience working on projects for both businesses and universities alike.

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Source: SwissCognitive

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