“Data is the new oil” by Clive Humby highlights the importance of data in the 21st century. In this context, data augmentation is one of the AI trends which solves the lack of data for machine learning tasks. It is even more critical when collecting large amounts of data is difficult, especially for tabular data. Moreover, synthetic data help to protect private information contained in the original data (e.g., medical records) while sharing it with other parties and the research community. Existing applications of synthetic data include deepfake[1], oversampling for imbalanced data, and public healthcare records.
More recently, generative models have emerged as the state-of-the-art technique in generating synthetic data, by discovering the pattern of the original data and generating new samples similar to the original data. In this article, we explore several recent studies on one of the most popular generative architectures, i.e., Generative Adversarial Network (GAN), for generating synthetic tabular data. We also discuss the challenges of the tabular data generation task and how GANs overcome these obstacles.
My original article is published in
Quant Blog of Quantmetry.
