# VIGAN: Missing View Imputation with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1708.06724 · HTML: https://mlsanity.com/paper/1708.06724/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2017.

## Built on by (most cited first, 30 of 33)

- [Multimodal Fusion on Low-quality Data: A Comprehensive Survey](https://mlsanity.com/paper/2404.18947/) (2024)
- [A Survey on Incomplete Multi-view Clustering](https://mlsanity.com/paper/2208.08040/) (2022)
- [Tag-assisted Multimodal Sentiment Analysis under Uncertain Missing Modalities](https://mlsanity.com/paper/2204.13707/) (2022)
- [Recent Advances in Multimodal Affective Computing: An NLP Perspective](https://mlsanity.com/paper/2409.07388/) (2024)
- [HetMAML: Task-Heterogeneous Model-Agnostic Meta-Learning for Few-Shot Learning Across Modalities](https://mlsanity.com/paper/2105.07889/) (2021)
- [Modern Views of Machine Learning for Precision Psychiatry](https://mlsanity.com/paper/2204.01607/) (2022)
- [What Makes for Robust Multi-Modal Models in the Face of Missing Modalities?](https://mlsanity.com/paper/2310.06383/) (2023)
- [Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance](https://mlsanity.com/paper/2405.10987/) (2024)
- [How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation](https://mlsanity.com/paper/2407.08442/) (2024)
- [Medical data wrangling with sequential variational autoencoders](https://mlsanity.com/paper/2103.07206/) (2021)
- [Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion](https://mlsanity.com/paper/2108.08301/) (2021)
- [Can Text-to-image Model Assist Multi-modal Learning for Visual Recognition with Visual Modality Missing?](https://mlsanity.com/paper/2402.09036/) (2024)
- [Multilevel Stochastic Optimization for Imputation in Massive Medical Data Records](https://mlsanity.com/paper/2110.09680/) (2021)
- [Towards Robust Multimodal Representation: A Unified Approach with Adaptive Experts and Alignment](https://mlsanity.com/paper/2503.09498/) (2025)
- [Towards Robust Optical-SAR Object Detection under Missing Modalities: A Dynamic Quality-Aware Fusion Framework](https://mlsanity.com/paper/2512.22447/) (2025)
- [Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations](https://mlsanity.com/paper/2111.02823/) (2021)
- [Imputation of missing values in multi-view data](https://mlsanity.com/paper/2210.14484/) (2022)
- [Self-supervised Multi-view Clustering in Computer Vision: A Survey](https://mlsanity.com/paper/2309.09473/) (2023)
- [Imputation-free Learning of Tabular Data with Missing Values using Incremental Feature Partitions in Transformer](https://mlsanity.com/paper/2504.14610/) (2025)
- [Hyper-modal Imputation Diffusion Embedding with Dual-Distillation for Federated Multimodal Knowledge Graph Completion](https://mlsanity.com/paper/2506.22036/) (2025)
- [Deep Adversarial Inconsistent Cognitive Sampling for Multi-view Progressive Subspace Clustering](https://mlsanity.com/paper/2101.03783/) (2021)
- [Towards a methodology for addressing missingness in datasets, with an application to demographic health datasets](https://mlsanity.com/paper/2211.02856/) (2022)
- [Ae2I: A Double Autoencoder for Imputation of Missing Values](https://mlsanity.com/paper/2301.06633/) (2023)
- [Neural Network Architecture for Database Augmentation Using Shared Features](https://mlsanity.com/paper/2302.01374/) (2023)
- [A Missing Data Imputation GAN for Character Sprite Generation](https://mlsanity.com/paper/2409.10721/) (2024)
- [Modular Deep Learning for Multivariate Time-Series: Decoupling Imputation and Downstream Tasks](https://mlsanity.com/paper/2411.03941/) (2024)
- [Class Similarity-Based Multimodal Classification under Heterogeneous Category Sets](https://mlsanity.com/paper/2506.09745/) (2025)
- [Missing-Modality-Aware Graph Neural Network for Cancer Classification](https://mlsanity.com/paper/2506.22901/) (2025)
- [An Interdisciplinary and Cross-Task Review on Missing Data Imputation](https://mlsanity.com/paper/2511.01196/) (2025)
- [Simple Yet Effective Selective Imputation for Incomplete Multi-view Clustering](https://mlsanity.com/paper/2512.10327/) (2025)

## Abstract (arXiv)

In an era when big data are becoming the norm, there is less concern with the quantity but more with the quality and completeness of the data. In many disciplines, data are collected from heterogeneous sources, resulting in multi-view or multi-modal datasets. The missing data problem has been challenging to address in multi-view data analysis. Especially, when certain samples miss an entire view of data, it creates the missing view problem. Classic multiple imputations or matrix completion methods are hardly effective here when no information can be based on in the specific view to impute data for such samples. The commonly-used simple method of removing samples with a missing view can dramatically reduce sample size, thus diminishing the statistical power of a subsequent analysis. In this paper, we propose a novel approach for view imputation via generative adversarial networks (GANs), which we name by VIGAN. This approach first treats each view as a separate domain and identifies domain-to-domain mappings via a GAN using randomly-sampled data from each view, and then employs a multi-modal denoising autoencoder (DAE) to reconstruct the missing view from the GAN outputs based on paired data across the views. Then, by optimizing the GAN and DAE jointly, our model enables the knowledge integration for domain mappings and view correspondences to effectively recover the missing view. Empirical results on benchmark datasets validate the VIGAN approach by comparing against the state of the art. The evaluation of VIGAN in a genetic study of substance use disorders further proves the effectiveness and usability of this approach in life science.