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Assessing Quality of Synthetic Data Compared to Real-World Datasets

Author(s) Sai Kalyani Rachapalli
Country United States
Abstract In the past few years, application of synthetic data has come to prominence in machine learning, artificial intelligence, and data science since it presents an alternative solution for addressing issues such as privacy concerns, unavailability of data, and costs associated with the acquisition of real-world data. The goal of this paper is to determine the quality of synthetic data against actual world datasets. With an extensive review of current methodologies, we compare synthetic data generation approaches and quality evaluation metrics. Based on our analysis, we expose the strengths and limitations of synthetic data, from applicability, scalability, and usability in practical applications. Synthetic data is compared between different applications such as image recognition, natural language processing, and autonomous vehicles. By looking into different approaches like generative adversarial networks (GANs), Variational Autoencoders (VAEs), and other synthetic data generation techniques, this paper aims to present findings on when synthetic data can be thought of as a suitable substitute for real-world data. Lastly, we present suggestions on how to make the best use of synthetic data in real-world applications.
Field Engineering
Published In Volume 3, Issue 6, June 2022
Published On 2022-06-03
Cite This Assessing Quality of Synthetic Data Compared to Real-World Datasets - Sai Kalyani Rachapalli - IJLRP Volume 3, Issue 6, June 2022. DOI 10.70528/IJLRP.v3.i6.1570
DOI https://doi.org/10.70528/IJLRP.v3.i6.1570
Short DOI https://doi.org/g9mvrf
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