How does flow-matching based synthetic data generation compare to CTGAN in improving minority class recall for

Abstract

Abstract: Handling imbalance in class distribution when building a classifier over tabular data has been a problem of long-standing interest. One popular approach is augmenting the training dataset with synthetically generated data. While classical augmentation techniques were limited to linear interpolation of existing minority class examples, recently higher capacity deep generative models are providing greater promise. However, handling of imbalance in class distribution when building a deep generative model is also a challenging problem, that has not been studied as extensively as imbalanced class

Research Question

How does flow-matching based synthetic data generation compare to CTGAN in improving minority class recall for imbalanced tabular datasets?

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