# Data Augmentation with Variational Autoencoder for Imbalanced Dataset

arXiv: https://arxiv.org/abs/2412.07039 · HTML: https://mlsanity.com/paper/2412.07039/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 9 Dec 2024.

## Builds on (most cited first, 10 of 10)

- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Delving into Deep Imbalanced Regression](https://mlsanity.com/paper/2102.09554/) (2021)
- [Balanced MSE for Imbalanced Visual Regression](https://mlsanity.com/paper/2203.16427/) (2022)
- [VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data](https://mlsanity.com/paper/2006.11941/) (2020)
- [RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression](https://mlsanity.com/paper/2205.15236/) (2022)
- [ConR: Contrastive Regularizer for Deep Imbalanced Regression](https://mlsanity.com/paper/2309.06651/) (2023)
- [UBL: an R package for Utility-based Learning](https://mlsanity.com/paper/1604.08079/) (2016)
- [Generative Oversampling for Imbalanced Data via Majority-Guided VAE](https://mlsanity.com/paper/2302.10910/) (2023)
- [Data Augmentation for Imbalanced Regression](https://mlsanity.com/paper/2302.09288/) (2023)
- [Progressive VAE Training on Highly Sparse and Imbalanced Data](https://mlsanity.com/paper/1912.08283/) (2019)

## Built on by (most cited first, 5 of 5)

- [Hybrid Imbalanced Regression Through Unified Data-Level and Algorithm-Level Balancing](https://mlsanity.com/paper/2606.01221/) (2026)
- [CARTGen-IR: Synthetic Tabular Data Generation for Imbalanced Regression](https://mlsanity.com/paper/2506.02811/) (2025)
- [Taming Data Challenges in ML-based Security Tasks Using Generative AI](https://mlsanity.com/paper/2507.06092/) (2025)
- [Synthetic data for ratemaking: imputation-based methods vs adversarial networks and autoencoders](https://mlsanity.com/paper/2509.02171/) (2025)
- [Close to Reality: Interpretable and Feasible Data Augmentation for Imbalanced Learning](https://mlsanity.com/paper/2603.13927/) (2026)

## Abstract (arXiv)

Learning from an imbalanced distribution presents a major challenge in predictive modeling, as it generally leads to a reduction in the performance of standard algorithms. Various approaches exist to address this issue, but many of them concern classification problems, with a limited focus on regression. In this paper, we introduce a novel method aimed at enhancing learning on tabular data in the Imbalanced Regression (IR) framework, which remains a significant problem. We propose to use variational autoencoders (VAE) which are known as a powerful tool for synthetic data generation, offering an interesting approach to modeling and capturing latent representations of complex distributions. However, VAEs can be inefficient when dealing with IR. Therefore, we develop a novel approach for generating data, combining VAE with a smoothed bootstrap, specifically designed to address the challenges of IR. We numerically investigate the scope of this method by comparing it against its competitors on simulations and datasets known for IR.