IEMEK Journal of Embedded Systems and Applications (대한임베디드공학회논문지)

Institute of Embedded Engineering of Korea (대한임베디드공학회)
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Methodology of Applying Randomness for Boosting Image Classification Performance

이미지 분류 성능 향상을 위한 무작위성 적용 방법론

  • Juyong Park (Inha University) ;
  • Yuri Jeon (Inha University) ;
  • Miyeong Kim (Inha University) ;
  • Jeongmin Lee (Inha University) ;
  • Yoonsuk Hyun (Inha University)
  • 박주용 ;
  • 전유리 ;
  • 김미영 ;
  • 이정민 ;
  • 현윤석
  • Received : 2024.06.28
  • Accepted : 2024.09.07
  • Published : 2024.10.31
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Abstract

Securing various types of training data in image Classification is important for improving performance. However, increasing the amount of original data is cost-limited, so data diversity can be secured by transforming images through data augmentation. Recently, a new deep learning approach using Generative AI models like GAN or Diffusion Based models has emerged in the Data Augmentation task, and reinforcement learning-based methods such as AutoAugment and Deep AutoAugment using existing basic Augmentation techniques are also showing good performance. However, this method has the disadvantage of having a complicated optimization procedure and high computational cost. This paper conducted various experiments with existing methods Cutmix, Mixup, RandAugment. By combining these techniques appropriately, we obtained higher performance than existing method without much effort. Additionally, our ablation experiment shows that additional hyper-parameter adjustments are needed for the basic augmentation types when we use RandAugment. Our code is available at https://github.com/lliee1/Randomness_Analysis.

Keywords

Acknowledgement

본 논문은 2022년, 2024년 정부 (과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임 (No. 2022R1A4A5033271, No.RS-2024-00348476).

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