Dalia Atif

HardTVAE: Hardness-Aware Generation and Multi-View Fidelity Evaluation of Synthetic Tabular Data for Imbalanced Learning

The proposed generative model and evaluation framework establish a structured basis for both enhancing and evaluating synthetic data under class imbalance and proposes a multi-view fidelity framework that integrates distributional, topological, complexity-based, and hardness-based perspectives to capture complementary aspects of data fidelity.