# Regression Augmentation With Data-Driven Segmentation

arXiv: https://arxiv.org/abs/2508.01455 · HTML: https://mlsanity.com/paper/2508.01455/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 60 references that are in the map.
- First posted on arXiv 2 Aug 2025.

## Builds on (most cited first, 17 of 17)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Delving into Deep Imbalanced Regression](https://mlsanity.com/paper/2102.09554/) (2021)
- [BAGAN: Data Augmentation with Balancing GAN](https://mlsanity.com/paper/1803.09655/) (2018)
- [Balanced MSE for Imbalanced Visual Regression](https://mlsanity.com/paper/2203.16427/) (2022)
- [Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning](https://mlsanity.com/paper/2008.09202/) (2020)
- [Data Augmentation Using GANs](https://mlsanity.com/paper/1904.09135/) (2019)
- [A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series](https://mlsanity.com/paper/1904.00575/) (2019)
- [A Survey of Predictive Modelling under Imbalanced Distributions](https://mlsanity.com/paper/1505.01658/) (2015)
- [Minimum Covariance Determinant and Extensions](https://mlsanity.com/paper/1709.07045/) (2017)
- [SMOTified-GAN for class imbalanced pattern classification problems](https://mlsanity.com/paper/2108.03235/) (2021)
- [Are we making much progress? Revisiting chemical reaction yield prediction from an imbalanced regression perspective](https://mlsanity.com/paper/2402.05971/) (2024)
- [Local distribution-based adaptive oversampling for imbalanced regression](https://mlsanity.com/paper/2504.14316/) (2025)
- [SMOGAN: Synthetic Minority Oversampling with GAN Refinement for Imbalanced Regression](https://mlsanity.com/paper/2504.21152/) (2025)

## Abstract (arXiv)

Imbalanced regression arises when the target distribution is skewed, causing models to focus on dense regions and struggle with underrepresented (minority) samples. Despite its relevance across many applications, few methods have been designed specifically for this challenge. Existing approaches often rely on fixed, ad hoc thresholds to label samples as rare or common, overlooking the continuous complexity of the joint feature-target space and fail to represent the true underlying rare regions. To address these limitations, we propose a fully data-driven GAN-based augmentation framework that uses Mahalanobis-Gaussian Mixture Modeling (GMM) to automatically identify minority samples and employs deterministic nearest-neighbour matching to enrich sparse regions. Rather than preset thresholds, our method lets the data determine which observations are truly rare. Evaluation on 32 benchmark imbalanced regression datasets demonstrates that our approach consistently outperforms state-of-the-art data augmentation methods.