• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid

References

1 
C. I. Moon, J. Lee, H. Yoo, Y. Baek, O. Lee, 2021, Optimization of psoriasis assessment system based on patch images, Scientific reports, Vol. 11, No. 1, pp. 1-13DOI
2 
C. W. Choi, B. R. Kim, S. Yang, S. W. Youn, 2019, Morphological Characteristics of Psoriatic Lesions Affect the Accuracy and Reliability of Severity Assessments: Proposal for New Working Criteria for the Psoriasis Area and Severity Index, Annals of Dermatology, Vol. 31, No. 1, pp. 81-83DOI
3 
I. S. A. Abdelhalim, M. F. Mohamed, Y. B. Mahdy, 2021, Data augmentation for skin lesion using self-attention based progressive generative adversarial network, Expert Systems with Applications, Vol. 165, No. 113922DOI
4 
J. N. Lee, H. C. Cho, 2021, Automated Polyp Detection System in Colonoscopy using Object Detection Algorithm based on Deep Learning, The transactions of The Korean Institute of Electrical Engineers, Vol. 70, No. 1, pp. 152-157Google Search
5 
C. Shorten, T. M. Khoshgoftaar, 2019, A survey on image data augmentation for deep learning, Journal of big data, Vol. 6 , No. 1, pp. 1-48DOI
6 
X. Wang, K. Wang, S. Lian, 2020, A survey on face data augmentation for the training of deep neural networks, Neural computing and applications, Vol. 32, No. 19, pp. 15503-15531DOI
7 
G. Haixiang, 2017, Learning from class-imbalanced data: Review of methods and applications, Expert Systems with Applications, Vol. 73, pp. 220-239DOI
8 
J. N. Lee, H. C. Cho, H. C. Cho, 2021, A Study on Data Augmentation Methods Optimized for Gastric Cancer Classification in Gastroscopy Images, The transactions of The Korean Institute of Electrical Engineers, Vol. 70, No. 12, pp. 2015-2021Google Search
9 
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, Q. V. Le, 2019, Autoaugment: Learning augmentation strategies from data, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 113-123Google Search
10 
E. D. Cubuk, B. Zoph, J. Shlens, Q. V. Le, 2020, Randaugment: Practical automated data augmentation with a reduced search space, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 702-703Google Search
11 
S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, Y. Yoo, 2019, Cutmix: Regularization strategy to train strong classifiers with localizable features, In Proceedings of the IEEE/CVF international conference on computer vision, pp. 6023-6032Google Search
12 
D. Hendrycks, T. Dietterich, 2019, Benchmarking neural network robustness to common corruptions and perturbations, arXiv preprint arXiv:1903.12261Google Search
13 
V. Verma, A. Lamb, C. Beckham, A. Najafi, I. Mitliagkas, D. Lopez-Paz, Y. Bengio, 2019, Manifold mixup: Better representations by interpolating hidden states, In International Conference on Machine Learning PMLR, pp. 6438-6447Google Search
14 
S. Back, S. Lee, S. Shin, Y. Yu, T. Yuk, S. Jong, K. Lee, 2021, Robust skin disease classification by distilling deep neural network ensemble for the mobile diagnosis of herpes zoster, IEEE Access, Vol. 9, pp. 20156-20169Google Search
15 
M. Tan, Q. Le, 2019, Efficientnet: Rethinking model scaling for convolutional neural networks, In International conference on machine learning PMLR, pp. 6105-6114Google Search
16 
T. He, Z. Zhang, H. Zhang, Z. Zhang, J. Xie, M. Li, 2019, Bag of tricks for image classification with convolutional neural networks, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 558-567Google Search
17 
L. Liu, H. Jiang, P. He, W. Chen, X. Liu, J. Gao, J. Han, 2019, On the variance of the adaptive learning rate and beyond, arXiv preprint arXiv:1908.03265Google Search