With recent advancements in machine learning (ML) and artificial intelligence (AI), having enough data to train the models is one of the steppingstones to a successful product launch.
Large datasets are the core for successful training and obtaining them can be challenging due to various reasons like availability or accessibility. For this reason, using synthetic data for training purposes can become a way out.
In this article we are analyzing how synthetic data are obtained, what challenges may occur in the process and what their advantages are.
What Is Synthetic Data and How Are They Made?
Basically, synthetic data is information usually generated with algorithms and not directly collected from real-world sources. In other words, synthetic datasets are designed to copy the features of the data inserted into the ML model or collected from actual life cases.
Even if there is no real-life data available, synthetic data can be created by simply providing the ML model with made-up information that does not have ties to a person or situation.
As to the main advantage, synthetic data are an immense source of data supply which is later used to train target ML models.
The first step in creating synthetic data is usually designing the patterns the data should follow. This process can range from generating completely artificial datasets to creating ones that closely resemble real-world data.
It all depends on the goal. Sometimes, synthetic data is directly modeled after existing datasets to ensure similarity, while in other cases, it is entirely independent.
Synthetic data has become an important solution to cover fields where obtaining data is challenging due to their nature. For instance, let’s take the healthcare or financial sector. For example, using real patient data can lead to ethical dilemmas, but synthetic data can provide similar cases without revealing sensitive information.
The main reasonsynthetic data are so highly valued among specialists is because they are of high quality and diverse,scalable, and efficient.
Since the scope of data used for ML models training grows quickly, it is extremely difficult to keep up with providing real-life data in such huge volumes.
Another benefit is that due to introduced algorithms, the generation of synthetic data does not contain errors or bias, which is crucial for final results.
TOP Techniques for Generating Synthetic Data
Today, we have a plethora of techniques for generating synthetic data. Let’s discuss the most common methods and where they are most frequently used:
- Hybrid Technique is a combination of synthetic and real-life data used to fill gaps and address inconsistencies in the data patterns.
- Data Augmentation allows transforming data by cropping them, rotating, or flipping to create different versions of the input data. This method is common when processing texts or images.
- Simulation Method allows data creation based on real-world scenarios previously integrated into the training process. This method normally includes the preset parameters and rules to follow when generating synthetic data. Advanced AI-driven simulations now can mimic complex environments, e.g., creating autonomous vehicle scenarios.
- Generative Method includes the two most common techniques, Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
GANs are one of the most popular tools for creating synthetic data which consist of two components: a generator for creating fake data and a discriminator that analyzes the generated data and differentiates them from real data.
Over time, the generator becomes better at creating realistic data, whether it is images, audio, etc. Although GANs were initially designed for image generation, today they are used for creating synthetic faces, objects, and datasets for ML tasks.
VAEs operate by studying the compressed representation of input data (latent space) and then using that representation to generate new data samples.
VAEs are especially useful for generating structured data, such as synthetic medical records or ECG waveforms, since capturing the underlying data distribution is of great significance.
How about the Challenges?
Despite its enormous potential, synthetic data still has several challenges. Low-quality synthetic data can lead to creating inaccurate models, especially if the generated data does not even remotely reflect real-world patterns. Biases in synthetic data can also raise ethical concerns, like stereotyping in decision-making models.
Secondly, while synthetic data might perform well in controlled environments, it may not work as well in real-world cases. Models trained on synthetic data can only struggle when faced with real examples, as they lack the detail of real-world diversity.
Thirdly, generating high-quality synthetic data, especially with techniques like GANs, can be quite expensive. Training these models requires significant resources, making it challenging for smaller organizations to adopt them.
The Future of Synthetic Data in ML
As generative techniques like GANs and VAEs continue to evolve, synthetic data is expected to play a pivotal role in taking machine learning to a new level. Synthetic data can help train models to withstand adversarial attacks by exposing them to a wider range of scenarios during training.
From healthcare to autonomous vehicles, synthetic data can provide domain-specific solutions where gathering real-world data is difficult or unsafe. Also, advances in synthetic data generation could help address biases in datasets, creating fairer and more accessible models.
Finally, synthetic data becomes an excellent instrument for secure data sharing, therefore, reducing privacy concerns.
Synthetic data is more than just a workaround for obtaining more data for ML model training. It is a powerful tool for transforming data science and machine learning.
Whether it is through generating diverse datasets or protecting sensitive information, synthetic data is a critical component for making machine learning more accessible, ethical, and effective.
