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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.12
- 수록면
- 1,598 - 1,605 (8page)
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초록· 키워드
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Recently, convolutional neural networks (CNNs) have been widely used with excellent performance in various computer vision fields, including super-resolution (SR). However, CNN is computationally intensive and requires a lot of memory, making it difficult to apply to limited hardware resources such as mobile or Internet of Things devices. To solve these limitations, network lightening studies have been actively conducted to reduce the depth or size of pre-trained deep CNN models while maintaining their performance as much as possible. This paper aims to lighten the SR CNN model, SRGAN, using the knowledge distillation among network lightening technologies; thus, it proposes four techniques with different methods of transferring the knowledge of the teacher network to the student network and presents experiments to compare and analyze the performance of each technique. In our experimental results, it was confirmed through quantitative and qualitative evaluation indicators that student networks with knowledge transfer performed better than those without knowledge transfer, and among the four knowledge transfer techniques, the technique of conducting adversarial learning after transferring knowledge from the teacher generator to the student generator showed the best performance.
목차
ABSTRACT
1. 서론
2. 관련 연구
3. 지식 전달 방법
4. 실험 및 분석
5. 결론
REFERENCE
참고문헌 (18)
참고문헌 신청
D. Lee, H.S. Lee, K. Lee, and H.-J. Lee, “Fast Very Deep Convolutional Neural Network with Deconvolution for Super-Resolution,” Journal of Korea Multimedia Society, Vol. 20, No. 11, pp. 1750-1758, 2017.
G. Hinton, O. Vinyals, and J. Dean, “Distilling the Knowledge in a Neural Network,” Proceeding of NIPS 2014 Deep Learning and Representation Learning Workshop, 2014.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A.P. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 105-114, 2017.
J. Gou, B. Yu, S.J. Maybank, and D. Tao “Knowledge Distillation: A Survey,” International J ournal of Computer Vision, Vol. 129, pp. 1789-1819, 2021.
A. Romero, N. Ballas, S.E. Kahou, A. Chassang, C. Gatta, and Y. Bengio, “FitNets: Hints for Thin Deep Nets,” Proceeding of the International Conference on Learning Representations, 2015.
G. Hinton, O. Vinyals, and J. Dean, “Distilling the Knowledge in a Neural Network,” Proceeding of NIPS 2014 Deep Learning and Representation Learning Workshop, 2014.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A.P. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 105-114, 2017.
J. Gou, B. Yu, S.J. Maybank, and D. Tao “Knowledge Distillation: A Survey,” International J ournal of Computer Vision, Vol. 129, pp. 1789-1819, 2021.
A. Romero, N. Ballas, S.E. Kahou, A. Chassang, C. Gatta, and Y. Bengio, “FitNets: Hints for Thin Deep Nets,” Proceeding of the International Conference on Learning Representations, 2015.
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