# ClueGAIN: Application of Transfer Learning On Generative Adversarial Imputation Nets (GAIN)

arXiv: https://arxiv.org/abs/2302.03140 · HTML: https://mlsanity.com/paper/2302.03140/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 6 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [Handling Missing Data with Graph Representation Learning](https://mlsanity.com/paper/2010.16418/) (2020)
- [MisGAN: Learning from Incomplete Data with Generative Adversarial Networks](https://mlsanity.com/paper/1902.09599/) (2019)
- [Neural Stain-Style Transfer Learning using GAN for Histopathological Images](https://mlsanity.com/paper/1710.08543/) (2017)
- [PC-GAIN: Pseudo-label Conditional Generative Adversarial Imputation Networks for Incomplete Data](https://mlsanity.com/paper/2011.07770/) (2020)
- [A Two Stage GAN for High Resolution Retinal Image Generation and Segmentation](https://mlsanity.com/paper/1907.12296/) (2019)
- [HexaGAN: Generative Adversarial Nets for Real World Classification](https://mlsanity.com/paper/1902.09913/) (2019)
- [Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network](https://mlsanity.com/paper/2006.11783/) (2020)
- [FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Prediction](https://mlsanity.com/paper/2203.04692/) (2022)
- [Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations](https://mlsanity.com/paper/2111.02823/) (2021)

## Abstract (arXiv)

Many studies have attempted to solve the problem of missing data using various approaches. Among them, Generative Adversarial Imputation Nets (GAIN) was first used to impute data with Generative Adversarial Nets (GAN) and good results were obtained. Subsequent studies have attempted to combine various approaches to address some of its limitations. ClueGAIN is first proposed in this study, which introduces transfer learning into GAIN to solve the problem of poor imputation performance in high missing rate data sets. ClueGAIN can also be used to measure the similarity between data sets to explore their potential connections.