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MisGAN: Learning from Incomplete Data with Generative Adversarial Networks

arXiv 1902.09599 ↗First posted 25 Feb 2019cs.LG stat.MLcited by 74

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Abstract (arXiv)

Generative adversarial networks (GANs) have been shown to provide an effective way to model complex distributions and have obtained impressive results on various challenging tasks. However, typical GANs require fully-observed data during training. In this paper, we present a GAN-based framework for learning from complex, high-dimensional incomplete data. The proposed framework learns a complete data generator along with a mask generator that models the missing data distribution. We further demonstrate how to impute missing data by equipping our framework with an adversarially trained imputer. We evaluate the proposed framework using a series of experiments with several types of missing data processes under the missing completely at random assumption.

Key facts. Cited by 74 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Feb 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 21; 2022: 9; 2023: 10; 2024: 12; 2025: 10; 2026: 12.

74 papers.

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  1. 2022SAITS: Self-Attention-based Imputation for Time Seriescited by 167
  2. 2023Ambient Diffusion: Learning Clean Distributions from Corrupted Datacited by 108
  3. 2022HyperImpute: Generalized Iterative Imputation with Automatic Model Selectioncited by 81
  4. 2023GSURE-Based Diffusion Model Training with Corrupted Datacited by 40
  5. 2023MissDiff: Training Diffusion Models on Tabular Data with Missing Valuescited by 38
  6. 2023Transformed Distribution Matching for Missing Value Imputationcited by 31
  7. 2021Identifiable Generative Models for Missing Not at Random Data Imputationcited by 30
  8. 2021Learning to Drop Points for LiDAR Scan Synthesiscited by 27
  9. 2021Assessing Fairness in the Presence of Missing Datacited by 24
  10. 2021Arbitrary Conditional Distributions with Energycited by 22
  11. 2021Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problemscited by 15
  12. 2024Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AIcited by 15
38 more
  1. 2021Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networkscited by 12
  2. 2022AR-NeRF: Unsupervised Learning of Depth and Defocus Effects from Natural Images with Aperture Rendering Neural Radiance Fieldscited by 12
  3. 2022Modeling Network-level Traffic Flow Transitions on Sparse Datacited by 12
  4. 2021Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Datacited by 10
  5. 2021Spatiotemporal Tensor Completion for Improved Urban Traffic Imputationcited by 9
  6. 2021Fairness in Missing Data Imputationcited by 8
  7. 2021Gradient Importance Learning for Incomplete Observationscited by 7
  8. 2024Review for Handling Missing Data with special missing mechanismcited by 7
  9. 2021EMFlow: Data Imputation in Latent Space via EM and Deep Flow Modelscited by 6
  10. 2024DiffImpute: Tabular Data Imputation With Denoising Diffusion Probabilistic Modelcited by 6
  11. 2025Missing Data Imputation by Reducing Mutual Information with Rectified Flowscited by 6
  12. 2024A Context-Aware Approach for Enhancing Data Imputation with Pre-trained Language Modelscited by 5
  13. 2025Robust Simulation-Based Inference under Missing Data via Neural Processescited by 5
  14. 2021Medical data wrangling with sequential variational autoencoderscited by 4
  15. 2021MisConv: Convolutional Neural Networks for Missing Datacited by 4
  16. 2021Variational Gibbs Inference for Statistical Model Estimation from Incomplete Datacited by 4
  17. 2022FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Predictioncited by 4
  18. 2024Knockout: A simple way to handle missing inputscited by 4
  19. 2021IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalancecited by 3
  20. 2022Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifierscited by 3
  21. 2024MagiNet: Mask-Aware Graph Imputation Network for Incomplete Traffic Datacited by 3
  22. 2025Deep learning with missing datacited by 3
  23. 2021Multilevel Stochastic Optimization for Imputation in Massive Medical Data Recordscited by 2
  24. 2023Adversarial Learning for Feature Shift Detection and Correctioncited by 2
  25. 2024Adapting to Shifting Correlations with Unlabeled Data Calibrationcited by 2
  26. 2025Incomplete Data, Complete Dynamics: A Diffusion Approachcited by 2
  27. 2026Missingness-aware Data Imputation via AI-powered Bayesian Generative Modelingcited by 2
  28. 2021MAIN: Multihead-Attention Imputation Networkscited by 1
  29. 2021Missingness Augmentation: A General Approach for Improving Generative Imputation Modelscited by 1
  30. 2021Unsupervised domain adaptation with non-stochastic missing datacited by 1
  31. 2021Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulationscited by 1
  32. 2022Revealing Unobservables by Deep Learning: Generative Element Extraction Networks (GEEN)cited by 1
  33. 2023Posterior Consistency for Missing Data in Variational Autoencoderscited by 1
  34. 2024Learning-Based WiFi Fingerprint Inpainting via Generative Adversarial Networkscited by 1
  35. 2024An End-to-End Model for Time Series Classification In the Presence of Missing Valuescited by 1
  36. 2025Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learningcited by 1
  37. 2025Impute-MACFM: Imputation based on Mask-Aware Flow Matchingcited by 1
  38. 2021Multiple Organ Failure Prediction with Classifier-Guided Generative Adversarial Imputation Networkscited by 0

50 most cited of 74 All 74 (JSON)