Synthetic Data I

Synthetic data is a powerful tool that can be used to train machine learning models without the need for real-world data. It can be useful in cases where real-world data is hard to come by or when it would be unethical to collect it. Synthetic data has played an important role in the field of artificial intelligence since technology and methods advances. As an example, we encourage you to have a look at the example images presented in the CycleGAN-and-pix2pix GitHub repository [1] to get some idea of what synthetic image data can look like. In the next part of this series we’ll discuss these methods, including CycleGANs and GANs in general.

Figure 1: Example image from the CycleGAN-and-pix2pix GitHub repository


There are many real-world use cases for synthetic data. Next to the use cases described throughout this blog post, Tesla’s simulation tool is another one worth mentioning. They use their simulation tool to create videos of scenarios that happen only very rarely in the real world and quantify data. Examples of videos published by Tesla’s simulation tool are [2] and [3].

Synthetic data provides many advantages for various study fields, such as time series, anomaly detection, computer vision, speech recognition, and natural language processing. Firstly, it can be generated in large quantities, which is crucial for training machine learning architectures. This can be especially beneficial for deep learning models since they require a large amount of data to achieve good performance. Another advantage is that it can be controlled to generate data with specific characteristics, such as certain types of noise and outliers. This type of data may help to make the model more robust.

Additionally, including a wide range of variations helps make the model more generalizable by testing and evaluating the model’s overall performance in a controlled and repeatable environment. Moreover, generating different types of data provides an opportunity to compare different models’ performance efficiently under different conditions. Another advantage of synthetic data is having a chance to identify and address the problems before applying them to the real world. Also, creating diverse data can help manage the bias that models trained on non-diverse data have.

There are some challenges to the creation of synthetic data. One of the main challenges with synthetic data is seen while implementing sensitive applications in fields such as healthcare, finance, or self-driving cars; it is determining how to generate diverse and representative data. Another challenge is ensuring that the data is representative of real-world data to deal with complex structures, such as images or speech, as it can be difficult to generate realistic samples accurately. Synthetic data must be able to capture the complexities of real-world data to lead to a less robust and accurate model. Despite the challenges, synthetic data is especially valuable to improve machine learning models in a wide range of applications where real-world data is scarce or difficult to obtain or the data’s characteristics need to be controlled. It overcomes the limits of real-world data and creates more accurate, robust, and generalizable models.

One of the fields in which synthetic data can be used is privacy and security. Privacy concerns in artificial intelligence have become a major issue since a large amount of data is being collected and stored worldwide. Synthetic data can solve the problem of privacy of individuals by using data with similar characteristics to real data without sensitive information. This is especially relevant in fields like healthcare and finance, where personal information is needed to be used for implementing the model but also needs to be protected. Also, the use of synthetic data mitigates bias in the models by including a diverse range of samples to ensure that the model is fair and unbiased. For instance, generating data from different demographic groups can help the model not to discriminate against certain groups. Some real-world data, such as traffic patterns, weather conditions, and economic trends, can be difficult or impossible to replicate because of their complex and dynamic structure, which depends on the environment. Moreover, rare or catastrophic events like natural disasters and accidents can count in cases where replicating the real data is a big challenge for training machine learning algorithms. The increasing amount of generated data has made it significantly difficult to label and annotate data manually. With the use of synthetic data, the generation of labeled data automatically speeds up the annotation process and saves time and money.

One of the important aspects of synthetic data is the evaluation of its quality. One way to evaluate the data quality is by using AI models to compare synthetic and real data by training the models on synthetic data and testing them on real data. Since AI models require labeled data in some situations, synthetic data must be created with consistency for the labels.

There may be some challenges in the creation of large and diverse datasets for studies in the field of Artificial Intelligence. Since collecting data from the real world is sometimes expensive and time-consuming, researchers and engineers may have to find a way to get data in a short time with limited sources. There are many ways to generate synthetic data, and one of the most popular approaches are generative models. These models can be trained on real-world data to generate new synthetic data similar to the training data. For instance, a model trained on the images of cats can be used to generate new cat images which seem realistic but are synthetic. Having representative data for the target population in real scenarios is also critical. For example, obtaining representative time series data on different seasons and rare events may be difficult. As for anomaly detection, it is crucial for time series in many applications, such as fraud detection, network security, and healthcare monitoring. Simulating anomalies with synthetic data to train the model for detection can be a good solution.

Considering the details mentioned above, synthetic data can be seen as a powerful tool that can be used in a wide range of applications. From training and testing machine learning models to simulating complex and dynamic environments, synthetic data can help to overcome the limitations of real-world data and drive innovation in many academic and industrial fields. As the field of machine learning continues to evolve and an increasing amount of data continues to be generated, synthetic data will play an increasingly important role in the field of machine learning and Artificial Intelligence.

In the part two of this series on synthetic data we are going to look at Generative Adversarial Networks (GANs) in more detail. Stay tuned for more exciting blog posts!

About the author

Ayşenur Gilik

I am a researcher at Pro2Future and working on sustainability with explainable AI. I am currently pursuing a Ph.D. at the Institute of Pervasive Computing at JKU in Linz, Austria. I finished my M.Sc. in Electronics Engineering at Kadir Has University in İstanbul, Türkiye, where I worked as a teaching assistant in Electrical-Electronics Engineering Department for four years. I have primarily worked on machine learning and Artificial Intelligence and their applications on different engineering problems. My professional interests are machine learning, artificial intelligence, computer vision, sustainability, transparent and trustworthy systems, and teaching; my personal interests are literature, cinema, writing, and photography.