Author

Min-Ji Kang

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Open access 2026

MSTabVAE: Multi-Step Latent Conditional Variational Autoencoder for Imbalanced Tabular Data Synthesis

MSTabVAE, a novel generative framework that extends TabNet, a deep learning architecture for tabular data, into a conditional variational autoencoder (CVAE) framework, and introduces a multi-step latent mapping strategy to capture complex feature relationships in heterogeneous tabular data.

Min-Ji Kang, Hyeryung Jang · IEEE Access · 0 citations

#artificial intelligence Preprint Apr 2026

MAST: Mask-Guided Attention Control for Training-Free Regional-Multi Style Transfer

MAST (Mask-Guided Attention Control for Training-Free Regional-Multi Style Transfer), a unified attention-control framework for frozen diffusion models, achieves the best average ArtFID, FID, and R-FID among all baselines, demonstrating regional style fidelity, content preservation, and scalability.

Dong-Sig Kang, Jae-Young Hwang, Junseo Park et al. · 0 citations