# HexaGAN: Generative Adversarial Nets for Real World Classification

arXiv: https://arxiv.org/abs/1902.09913 · HTML: https://mlsanity.com/paper/1902.09913/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2019.

## Built on by (most cited first, 10 of 10)

- [Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set Framework](https://mlsanity.com/paper/2108.12159/) (2021)
- [FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Prediction](https://mlsanity.com/paper/2203.04692/) (2022)
- [Generative Adversarial Classification Network with Application to Network Traffic Classification](https://mlsanity.com/paper/2303.10681/) (2023)
- [Evaluation of Point Pattern Features for Anomaly Detection of Defect within Random Finite Set Framework](https://mlsanity.com/paper/2102.01882/) (2021)
- [Stein Latent Optimization for Generative Adversarial Networks](https://mlsanity.com/paper/2106.05319/) (2021)
- [On the link between generative semi-supervised learning and generative open-set recognition](https://mlsanity.com/paper/2303.11702/) (2023)
- [OASIS: Harnessing Diffusion Adversarial Network for Ocean Salinity Imputation using Sparse Drifter Trajectories](https://mlsanity.com/paper/2508.21570/) (2025)
- [ClueGAIN: Application of Transfer Learning On Generative Adversarial Imputation Nets (GAIN)](https://mlsanity.com/paper/2302.03140/) (2023)
- [A Survey and Benchmarking of Spatial-Temporal Traffic Data Imputation Models](https://mlsanity.com/paper/2412.04733/) (2024)
- [Electric Vehicle Identification from Behind Smart Meter Data](https://mlsanity.com/paper/2509.19316/) (2025)

## Abstract (arXiv)

Most deep learning classification studies assume clean data. However, when dealing with the real world data, we encounter three problems such as 1) missing data, 2) class imbalance, and 3) missing label problems. These problems undermine the performance of a classifier. Various preprocessing techniques have been proposed to mitigate one of these problems, but an algorithm that assumes and resolves all three problems together has not been proposed yet. In this paper, we propose HexaGAN, a generative adversarial network framework that shows promising classification performance for all three problems. We interpret the three problems from a single perspective to solve them jointly. To enable this, the framework consists of six components, which interact with each other. We also devise novel loss functions corresponding to the architecture. The designed loss functions allow us to achieve state-of-the-art imputation performance, with up to a 14% improvement, and to generate high-quality class-conditional data. We evaluate the classification performance (F1-score) of the proposed method with 20% missingness and confirm up to a 5% improvement in comparison with the performance of combinations of state-of-the-art methods.