Gamin: Generative adversarial multiple imputation network for highly missing data

Seongwook Yoon, Sanghoon Sull

    Research output: Contribution to journalConference articlepeer-review

    50 Citations (Scopus)

    Abstract

    We propose a novel imputation method for highly missing data. Though most existing imputation methods focus on moderate missing rate, imputation for high missing rate over 80% is still important but challenging. As we expect that multiple imputation is indispensable for high missing rate, we propose a generative adversarial multiple imputation network (GAMIN) based on generative adversarial network (GAN) for multiple imputation. Compared with similar imputation methods adopting GAN, our method has three novel contributions: 1) We propose a novel imputation architecture which generates candidates of imputation. 2) We present a confidence prediction method to perform reliable multiple imputation. 3) We realize them with GAMIN and train it using novel loss functions based on the confidence. We synthesized highly missing datasets using MNIST and CelebA to perform various experiments. The results show that our method outperforms baseline methods at high missing rate from 80% to 95%.

    Original languageEnglish
    Article number9156609
    Pages (from-to)8453-8461
    Number of pages9
    JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    DOIs
    Publication statusPublished - 2020
    Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States
    Duration: 2020 Jun 142020 Jun 19

    Bibliographical note

    Publisher Copyright:
    ©2020 IEEE.

    ASJC Scopus subject areas

    • Software
    • Computer Vision and Pattern Recognition

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