# PC-GAIN: Pseudo-label Conditional Generative Adversarial Imputation Networks for Incomplete Data

arXiv: https://arxiv.org/abs/2011.07770 · HTML: https://mlsanity.com/paper/2011.07770/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2020.

## Built on by (most cited first, 15 of 15)

- [MissDiff: Training Diffusion Models on Tabular Data with Missing Values](https://mlsanity.com/paper/2307.00467/) (2023)
- [Diffusion Models for Tabular Data Imputation and Synthetic Data Generation](https://mlsanity.com/paper/2407.02549/) (2024)
- [FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Prediction](https://mlsanity.com/paper/2203.04692/) (2022)
- [Incomplete Graph Learning: A Comprehensive Survey](https://mlsanity.com/paper/2502.12412/) (2025)
- [Adversarial Learning for Feature Shift Detection and Correction](https://mlsanity.com/paper/2312.04546/) (2023)
- [Generative Adversarial Networks for Imputing Sparse Learning Performance](https://mlsanity.com/paper/2407.18875/) (2024)
- [Boundary-enhanced time series data imputation with long-term dependency diffusion models](https://mlsanity.com/paper/2501.06585/) (2025)
- [Impute-MACFM: Imputation based on Mask-Aware Flow Matching](https://mlsanity.com/paper/2509.23126/) (2025)
- [Semi-supervised Learning with Missing Values Imputation](https://mlsanity.com/paper/2106.01708/) (2021)
- [Missingness Augmentation: A General Approach for Improving Generative Imputation Models](https://mlsanity.com/paper/2108.02566/) (2021)
- [Explainable Global Error Weighted on Feature Importance: The xGEWFI metric to evaluate the error of data imputation and data augmentation](https://mlsanity.com/paper/2206.08980/) (2022)
- [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)
- [BlockEcho: Retaining Long-Range Dependencies for Imputing Block-Wise Missing Data](https://mlsanity.com/paper/2402.18800/) (2024)
- [Generative Data Imputation for Sparse Learner Performance Data Using Generative Adversarial Imputation Networks](https://mlsanity.com/paper/2503.18982/) (2025)

## Abstract (arXiv)

Datasets with missing values are very common in real world applications. GAIN, a recently proposed deep generative model for missing data imputation, has been proved to outperform many state-of-the-art methods. But GAIN only uses a reconstruction loss in the generator to minimize the imputation error of the non-missing part, ignoring the potential category information which can reflect the relationship between samples. In this paper, we propose a novel unsupervised missing data imputation method named PC-GAIN, which utilizes potential category information to further enhance the imputation power. Specifically, we first propose a pre-training procedure to learn potential category information contained in a subset of low-missing-rate data. Then an auxiliary classifier is determined using the synthetic pseudo-labels. Further, this classifier is incorporated into the generative adversarial framework to help the generator to yield higher quality imputation results. The proposed method can improve the imputation quality of GAIN significantly. Experimental results on various benchmark datasets show that our method is also superior to other baseline approaches. Our code is available at urlhttps://github.com/WYu-Feng/pc-gain.