A Survey of Missing Data Imputation Using Generative Adversarial Networks

Jaeyoon Kim, Donghyun Tae, Junhee Seok

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    30 Citations (Scopus)

    Abstract

    Recently, many deep learning models for missing data imputation have been studied. One of the most popular models is Generative Adversarial Networks (GANs), which generate plausible fake data through adversarial training. In this paper, we take a look at the architecture, objective of a generator and a discriminator, training method and loss function. After that, we can see what improvements have been made to each model. Moreover, we can easily compare several GAN-based models for missing data imputation.

    Original languageEnglish
    Title of host publication2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages454-456
    Number of pages3
    ISBN (Electronic)9781728149851
    DOIs
    Publication statusPublished - 2020 Feb 1
    Event2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 - Fukuoka, Japan
    Duration: 2020 Feb 192020 Feb 21

    Publication series

    Name2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

    Conference

    Conference2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
    Country/TerritoryJapan
    CityFukuoka
    Period20/2/1920/2/21

    Bibliographical note

    Publisher Copyright:
    © 2020 IEEE.

    Keywords

    • Adversarial training
    • Discriminator
    • Generative Adversarial Networks (GANs)
    • Generator
    • Missing data imputation

    ASJC Scopus subject areas

    • Information Systems and Management
    • Artificial Intelligence
    • Computer Networks and Communications
    • Computer Vision and Pattern Recognition
    • Information Systems
    • Signal Processing

    Fingerprint

    Dive into the research topics of 'A Survey of Missing Data Imputation Using Generative Adversarial Networks'. Together they form a unique fingerprint.

    Cite this