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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.12 No.1
- 발행연도
- 2023.2
- 수록면
- 48 - 54 (7page)
- DOI
- 10.5573/IEIESPC.2023.12.1.48
이용수
초록· 키워드
오류제보하기
Deep learning models for image classification with adequate parameters show excellent classification performance because they can effectively extract the features of input images. On the other hand, there is a limit to the abilities of deep learning models to interpret images using only spatial information because an image is a signal with great spatial redundancy. Therefore, in this study, the discrete cosine transform was applied to an input image in units of an N×N block size to allow the deep learning model to employ both frequency and spatial information. The proposed method was implemented and verified by selecting a vision transformer using a 16×16 nonoverlapping patch as a baseline and training various datasets of Cifar-10, Cifar-100, and Tiny-ImageNet from the very beginning without pre-trained weights. The experimental results showed that the top-1 accuracy is improved by approximately 3-5% for every dataset with little increase in computational cost.
목차
Abstract
1. Introduction
2. Background
3. Proposed Method
4. Experimental Results
5. Conclusion
References
참고문헌 (31)
참고문헌 신청
A. Dosovitskiy et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020. Article (CrossRef Link)
Z. Dai, H. Liu, Q. V. Le, and M. Tan, “Coatnet: Marrying convolution and attention for all data sizes,” in Proc. Advances in Neural Information Processing Systems, 2021, vol. 34, pp. 3965-3977. Article (CrossRef Link)
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou, “Training data-efficient image transformers & distillation through attention,” in Proceedings of the International Conference on Machine Learning, 2021, pp. 10347-10357. Article (Cros
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” arXiv preprint arXiv:2111.06377, 2021. Article (CrossRef Link)
Z. Liu et al., “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 10012-10022. Article (CrossRef Link)
Z. Dai, H. Liu, Q. V. Le, and M. Tan, “Coatnet: Marrying convolution and attention for all data sizes,” in Proc. Advances in Neural Information Processing Systems, 2021, vol. 34, pp. 3965-3977. Article (CrossRef Link)
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou, “Training data-efficient image transformers & distillation through attention,” in Proceedings of the International Conference on Machine Learning, 2021, pp. 10347-10357. Article (Cros
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” arXiv preprint arXiv:2111.06377, 2021. Article (CrossRef Link)
Z. Liu et al., “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 10012-10022. Article (CrossRef Link)
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