Employing Deep Learning Using Synthetic Data Generation

We define synthetic data as the artificial data generated by using various algorithms that copy statistical properties of the real data but do not expose personal information.

In 2024, up to 60% of data required in the development of Artificial Intelligence and Analysis will be synthetic because of the rapid advancements witnessed in the field. Even though real data is more usable than synthetic data, there are several instances where synthetic data is equally valuable. Some of the latest cases involving the synthesization of data show an accuracy rate similar to training conducted using real data. Where organizations want to train their machine learning algorithms but have imbalanced data, the generation of synthetic data can aid in creating precise machine learning models. If the needed information can be located online, institutions do not have to create it. With the help of web crawlers, extraction of data related to online business platforms is doable and put in the desired format.

How Businesses Develop Synthetic Data For Deep Learning

Businesses may adopt various methods, including decision trees, techniques for deep learning, and iterative proportional fitting to execute the processes for data synthesis. The method chosen should consider the requirement of synthetic data generation and the amount of data utility required for the special role of data generation.
After synthesizing data, there should be an assessment of the use of synthetic data compared to real data. The stages involved in the process of utility assessment include:

• General purpose comparisons - Putting parameters like distributions and the correlation coefficients into comparison and using measurements from two datasets
• Workload-aware utility assessment - Differentiating the correctness of the output data from specific usage by analyzing the synthetic data.

Benefits Of Synthetic Data

Rules and regulations constrain real data usage, but synthetic data can copy the vital statistical information of the accurate data without exposure.

They can create data to simulate conditions that are yet to be encountered. Where real data is lacking, synthetic data is the only option.

Freedom from problems associated with statistics: They include skipping of patterns and nonresponse of items, among others.

Synthetic data preserves the relationship of variables in place of specific statics.

Applications Of Synthetic Data

• Financial services - Identifying fraudulent activities plays a big role in financial services. Although rare, synthetic fraud data is used when creating newer methods for detecting fraud.
• Healthcare - With the help of synthetic data, healthcare professionals can permit the external and internal use of recorded data while safeguarding the patient's identity. However, this applies widely to health facilities where the  customers' data is discreet. When doing clinical trials, synthetic data is used for tests where real data does not exist.
• Robotics and Automotive - Research aimed at developing automatic things like drones, self-drive cars, and robots was among the pioneers in the usage of synthetic data. Testing robots in real-life is very expensive and time-consuming. Synthetic data allows institutions to do tests for robot improvement on many simulations, aiding in costly real-life testing.
• Security - Institutions train and create neural network models for accurate image recognition in video surveillance cameras. This is quite costly. Synthetic data lowers the cost of training these models.