Earticle

초록

영어

Data augmentation has been employed in neural networks for building robust models, not exclusively in the training phase but also in the testing stage, where the predictions of every transformed image are aggregated to a greater lustiness and upgraded accuracy. Furthermore, deep learning approaches applied in data augmentation, namely adversarial training, GANs, and Neural Style Transfer were applied while training the models, neither while testing them. In this work, we present a study of applying test-time Neural Style Transfer transformation in medical images as a method of augmentation in test time. Besides, we display the experiment's results of a classification task. Results reveal that the synthesized samples employed as modified images in the test time significantly improved the performance of the classification model.

목차

Abstract
I. INTRODUCTION
II. RELATED WORKS
A. Neural Style Transfer
B. Test-Time Augmentation
III. DATASETS AND EXPERIMENTAL RESULTS
A. Dataset
B. Data generation
C. Classification Evaluation
IV. CONCLUSION
REFERENCES

키워드

저자

• Zineb Tissir [ Department of AI Software Gachon University ]
• Sang-Woong Lee [ Department of Software Gachon University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004