Automatic Data Augmentation Method with Improved Interpretability for Image Classification in Computer Vision Applications

Dair Ungarbayev, Osman Demirel, Muhammad Tahir Akhtar

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

Abstract

This paper presents an interpretable automatic data augmentation method. While the mechanism of the existing automatic augmentation methods is not easily understandable, the proposed method seeks to make the process of finding an augmentation policy more clear while maintaining simplicity. The proposed method has been tested on the Fashion-MNIST with a simple convnet and on the CIFAR10 dataset with ResNet50 network. Moreover, it was compared with the RandAugment, Augmix and TrivialAugment methods using benchmark datasets Fashion MNIST-C and CIFAR-10C. The developed method ex-hibits comparable results to that of existing approaches with the key advantage of interpretability.

Original languageEnglish
Title of host publicationProceedings of 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1356-1361
Number of pages6
ISBN (Electronic)9786165904773
DOIs
Publication statusPublished - 2022
Event2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022 - Chiang Mai, Thailand
Duration: Nov 7 2022Nov 10 2022

Publication series

NameProceedings of 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022

Conference

Conference2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022
Country/TerritoryThailand
CityChiang Mai
Period11/7/2211/10/22

Keywords

  • Automatic data augmentation
  • Image classification
  • Robustness

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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