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View all- Li SWu CXiong N(2022)Hybrid Architecture Based on CNN and Transformer for Strip Steel Surface Defect ClassificationElectronics10.3390/electronics1108120011:8(1200)Online publication date: 9-Apr-2022
Evaluation of Visualization Methods' Effect on Convolutional Neural Networks Research
Authors: 
Haijing Tang, Yiru Wang, Xu YangAuthors Info & Claims
Haijing Tang, Yiru Wang, Xu YangAuthors Info & ClaimsArticle No.: 52, Pages 1 - 5
Abstract
In recent years, as an important research hotspot in the field of artificial intelligence and machine learning, the convolutional neural network has made substantial breakthroughs and has been widely used. In order to better explore and understand its structure, more and more researchers have shifted the focus of their research to the visualization of convolutional neural networks. They learned from the neural network what features were studied. They applied it to parameter adjustment and optimization in the convolutional neural networks and achieved good results. In this paper, the basic structure of convolutional neural network is described first. Secondly, some commonly used volume and neural network models are introduced. Finally, the convolutional neural network visualization technology is evaluated.
References
[1]
HUBEL D H, WIESEL T N. Receptive fields, binocular interaction, and functional architecture in the cat's visual cortex{J}. Journal of Physiology, 1962, 160 (1): 106--154.
[2]
FUKUSHIMA K. Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position{J}. Biological Cybernetics, 1980, 36 (4): 193--202.
[3]
LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition{J}. Proceedings of the IEEE, 1998, 86 (11): 2278--2324.
[4]
SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition {EB/OL}. {2015-11-04}.
[5]
SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions {C}// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2015: 1--8.
[6]
HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition {EB/OL}. {2016-01-04}.
[7]
Hsu S H, Bayarsaikhan B E. Factors influencing on online shopping attitude and intention of mongolian consumers, The Journal of International Management Studies, vol. 7, no. 2, pp. 167--176, 2012.
[8]
Krizhevsky, A, Sutskever, I, Hinton, G: Imagenet classification with deep convolutional neural networks. In: NIPS 2012.
[9]
Zeiler, Matthew D and Fergus, Rob. Visualizing and understanding convolutional neural networks. arXiv preprint arXiv:1311.2901, 2013.
[10]
KINGMA D P, BA J. Adam: A method for stochastic optimization, CoRR, vol. abs/1412.6980, 2014.
[11]
GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation, IEEE Conference on Computer Vision and Pattern Recognition, 2014.
[12]
MASAYUKI, KOBAYASHI, MASANORI, et al. Generative Adversarial Network for Visualizing Convolutional Network{J}. IEEE 10th International Workshop on Computational Intelligence and Applications, 2017.
[13]
SZEGEDY C, ZAREMBA W, SUTSKEVER I, et al. Intriguing properties of neural networks, 2014.
[14]
LIU M, SHI J, LI Z, et al. Towards Better Analysis of Deep Convolutional Neural Networks, IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS VOL. 23, NO. 1--2017.
[15]
QIN Z, XU Z, DONG Q, et al.VoCaM: Visualization Oriented Convolutional Neural, 978-1-5386-3093-8, 2017.
[16]
WEI D, ZHOU B, TRRALBA A, et al. Freeman. Understanding intra-class knowledge inside CNN, CoRR, vol. abs/1507.02379, 2015.
[17]
MAHENDRAN A, VEDALDI A. Visualizing deep convolutional neural networks using natural pre-images, International Journal of Computer Vision, pp. 1--23, 2016.
[18]
IOFFE S, SZEGEDY C. Batch normalization: Accelerating deep network training by reducing internal covariate shift, in Proceedings of the 32nd International Conference on Machine Learning (ICML), 2015, pp. 448--456.
[19]
JIA Y, SHELHAMER E, DONAHUE J, et al. Guadarrama, and T. Darrell.Caffe: Convolutional architecture for fast feature embedding, arXiv preprint arXiv:1408.5093, 2014.
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- Evaluation of Visualization Methods' Effect on Convolutional Neural Networks Research
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Published In
December 2018
460 pages
ISBN:9781450366250
DOI:10.1145/3302425
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In-Cooperation
- The Hong Kong Polytechnic: The Hong Kong Polytechnic University
- City University of Hong Kong: City University of Hong Kong
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 21 December 2018
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- Research-article
- Research
- Refereed limited
Funding Sources
- National Key R&D Program of China
- National Natural Science Foundation of China
Conference
ACAI 2018
ACAI 2018: 2018 International Conference on Algorithms, Computing and Artificial Intelligence
December 21 - 23, 2018
Sanya, China
Acceptance Rates
ACAI '18 Paper Acceptance Rate 76 of 192 submissions, 40%;
Overall Acceptance Rate 173 of 395 submissions, 44%
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View all- Li SWu CXiong N(2022)Hybrid Architecture Based on CNN and Transformer for Strip Steel Surface Defect ClassificationElectronics10.3390/electronics1108120011:8(1200)Online publication date: 9-Apr-2022
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