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This article provides a comprehensive synthesis of the recent developments in
synthetic data generation via deep generative models, focusing on tabular
datasets. We specifically outline the importance of synthetic data generation
in the context of privacy-sensitive data. Additionally, we highlight the
advantages of using deep generative models over other methods and provide a
detailed explanation of the underlying concepts, including unsupervised
learning, neural networks, and generative models. The paper covers the
challenges and considerations involved in using deep generative models for
tabular datasets, such as data normalization, privacy concerns, and model
evaluation. This review provides a valuable resource for researchers and
practitioners interested in synthetic data generation and its applications.
