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The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.
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  4. 10.5370/KIEE.2022.71.12.1825
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References

1 
Y. LeCun, Y. Bengio, G. Hinton, 2015, Deep learning, Nature, Vol. 521, pp. 436-444DOI
2 
A. Krizhevsky, I. Sutskever, G. E Hinton, 2012, Imagenet classification with deep convolutional neural networks, In Advances in neural information processing systems, pp. 1097-1105Google Search
3 
K. Simonyan, A. Zisserman, 2014, Very Deep Convolutional Networks for Large-Scale Image Recognition, International Conference on Learning RepresentationsGoogle Search
4 
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, 2015, Going Deeper with Convolutions, Computer Vision and Pattern RecognitionGoogle Search
5 
R. Varshavsky, A. Gottlieb, M. Linial, D. Horn, 2006, Novel unsupervised feature filtering of biological data, Bioinformatics, Vol. 22, No. 14, pp. e507-e513DOI
6 
S Lee, K. Seo, 2020, Lightweight Deep Learning for Edge Computing, Proceedings of KIEE Summer Congerence.Google Search
7 
Z. Liu, J. Li, Z. Shen, G. Huang, S. Yan, C. Zhang, 2017, Learning efficient convolutional networks through network slimming, In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2736-2744Google Search
8 
Y. Zhang, C. Zhao, B. Ni, J. Zhang, H. Deng, 2019, Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks, arXiv preprint arXiv:1908.0262Google Search
9 
X. Dong, J. Huang, Y. Yang, S. Yan, 2017, More is less: A more complicated network with less inference complexity, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5840-5848Google Search
10 
Y. He, G. Kang, X. Dong, Y. Fu, Y. Yang, 2018, Soft fillter pruning for accelerating deep convolutional neural networks, arXiv preprint arXiv:1808.06866Google Search
11 
B. Sareni, L. Krahenbuhl, 1998, Fitness Sharing and Niching Methods Revisited, IEEE Transactions on Evolutionary Comutation, Vol. 2, No. 3, pp. 97-106DOI
12 
The CIFAR-10/100 dataset, https://www.cs.toronto.edu/~kriz/cifar.htmlGoogle Search
13 
The ImageNet dataset, https://www.image-net.org/Google Search
14 
K. Simonyan, A. Zisserman, 2014, Very Deep Convolutional Networks for Large-Scale Image Recognition, International Conference on Learning RepresentationsGoogle Search
15 
K. He, X. Zhang, S. Ren, J. Sun, 2016, Deep Residual Learning for Image Recognition, Computer Vision and Pattern RecognitionGoogle Search