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

arXiv 2302.03140 ↗First posted 6 Feb 2023cs.LGcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 29 references that are in the map. First posted on arXiv 6 Feb 2023.

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12 of 29 references.

References · 12

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014How transferable are features in deep neural networks?cited by 1,216
  3. 2018GAIN: Missing Data Imputation using Generative Adversarial Netscited by 349
  4. 2020Handling Missing Data with Graph Representation Learningcited by 132
  5. 2019MisGAN: Learning from Incomplete Data with Generative Adversarial Networkscited by 74
  6. 2017Neural Stain-Style Transfer Learning using GAN for Histopathological Imagescited by 18
  7. 2020PC-GAIN: Pseudo-label Conditional Generative Adversarial Imputation Networks for Incomplete Datacited by 15
  8. 2019A Two Stage GAN for High Resolution Retinal Image Generation and Segmentationcited by 12
  9. 2019HexaGAN: Generative Adversarial Nets for Real World Classificationcited by 10
  10. 2020Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Networkcited by 5
  11. 2022FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Predictioncited by 4
  12. 2021Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulationscited by 1

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