# Regression generation adversarial network based on dual data evaluation strategy for industrial application

arXiv: https://arxiv.org/abs/2512.19232 · HTML: https://mlsanity.com/paper/2512.19232/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 22 Dec 2025.

## Builds on (most cited first, 7 of 7)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [A Survey on Deep Semi-supervised Learning](https://mlsanity.com/paper/2103.00550/) (2021)
- [A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications](https://mlsanity.com/paper/2001.06937/) (2020)
- [Graph-based Semi-supervised Learning: A Comprehensive Review](https://mlsanity.com/paper/2102.13303/) (2021)
- [Active Learning for Regression Using Greedy Sampling](https://mlsanity.com/paper/1808.04245/) (2018)
- [Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active Learning](https://mlsanity.com/paper/2104.11952/) (2021)
- [Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression](https://mlsanity.com/paper/2003.11786/) (2020)

## Abstract (arXiv)

Soft sensing infers hard-to-measure data through a large number of easily obtainable variables. However, in complex industrial scenarios, the issue of insufficient data volume persists, which diminishes the reliability of soft sensing. Generative Adversarial Networks (GAN) are one of the effective solutions for addressing insufficient samples. Nevertheless, traditional GAN fail to account for the mapping relationship between labels and features, which limits further performance improvement. Although some studies have proposed solutions, none have considered both performance and efficiency simultaneously. To address these problems, this paper proposes the multi-task learning-based regression GAN framework that integrates regression information into both the discriminator and generator, and implements a shallow sharing mechanism between the discriminator and regressor. This approach significantly enhances the quality of generated samples while improving the algorithm's operational efficiency. Moreover, considering the importance of training samples and generated samples, a dual data evaluation strategy is designed to make GAN generate more diverse samples, thereby increasing the generalization of subsequent modeling. The superiority of method is validated through four classic industrial soft sensing cases: wastewater treatment plants, surface water, CO2 absorption towers, and industrial gas turbines.