A Conditional GAN for Tabular Data Generation with Probabilistic Sampling of Latent Subspaces
arXiv:2508.00472v2 Announce Type: replace Abstract: The tabular form constitutes the standard way of representing data in relational database systems and spreadsheets. But, similarly to other forms, tabular data suffers from class imbalance, a problem that causes serious performance degradation in a wide variety of machine learning tasks. One of the most effective solutions dictates the usage of Generative Adversarial Networks (GANs) in order to synthesize artificial data instances for the under-represented classes. Despite their good performance, none of the proposed GAN models takes into acc
The continuous evolution of AI research pushes for more efficient and robust methods for data synthesis, addressing challenges like class imbalance in real-world datasets.
Improving synthetic data generation directly enhances the performance of machine learning models in critical applications where real-world data is scarce or imbalanced.
This advancement provides a more sophisticated approach to creating synthetic tabular data, potentially leading to fairer and more accurate AI systems across various domains.
- · AI researchers
- · Data scientists
- · Industries with imbalanced datasets
- · Machine learning platforms
- · Traditional data augmentation methods
- · Systems heavily reliant on perfectly balanced datasets
Improved synthetic data generation for machine learning models, particularly for under-represented classes.
Enhanced fairness and accuracy of AI applications in domains with inherent data imbalance, such as medical diagnostics or fraud detection.
Reduced reliance on vast amounts of real-world annotated data, potentially accelerating AI development in data-scarce fields and ethical data sharing.
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Read at arXiv cs.LG