An Effective Supplementation of Insufficient Data by Generative Adversarial Networks

Abdulkabir Abdulraheem, Im Y. Jung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Generative Adversarial Networks (GANs) can be used for data augmentation in order to improve the outcome and performance of machine learning models for automatic information retrieval. We looked into the challenge faced with limited blurry and distorted digit images from expiry dates datasets, which is required to improve digit recognition tasks on medicine, consumables, cosmetic products and tube-type ointments. For our dataset, Wasserstein GAN with a gradient norm penalty (WGAN-GP) was effective for data augmentation among the state-of-the-art GANs by visible inspection and Fréchet Inception Distance (FID) value comparison.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE/ACM 9th International Conference on Big Data Computing, Applications and Technologies, BDCAT 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages174-175
Number of pages2
ISBN (Electronic)9781665460903
DOIs
StatePublished - 2022
Event9th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2022 - Vancouver, United States
Duration: 6 Dec 20229 Dec 2022

Publication series

NameProceedings - 2022 IEEE/ACM 9th International Conference on Big Data Computing, Applications and Technologies, BDCAT 2022

Conference

Conference9th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2022
Country/TerritoryUnited States
CityVancouver
Period6/12/229/12/22

Keywords

  • Automatic Information Retrieval
  • Data Augmentation
  • Generative Adversarial Networks
  • Machine learning

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