Open Access
Issue
MATEC Web Conf.
Volume 309, 2020
2019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
Article Number 03002
Number of page(s) 7
Section Smart Algorithms and Recognition
DOI https://doi.org/10.1051/matecconf/202030903002
Published online 04 March 2020
  1. G. E. Hinton, R.R.S., Reducing the Dimensionality of Data with Neural Networks. Sciencce, 2006. 313(5786): p. 504–507. [NASA ADS] [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  2. Lecun, Y., et al., Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998. 86(11): p. 2278–2324. [CrossRef] [Google Scholar]
  3. Krizhevsky, A., I. Sutskever and G.E. Hinton. ImageNet classification with deep convolutional neural networks. in International Conference on Neural Information Processing Systems. 2012. pages 1106-1114. [Google Scholar]
  4. Szegedy, C., et al., Going deeper with convolutions. 2014(9): p. 1–9. [Google Scholar]
  5. Simonyan, K. and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition. Computer Science, 2014: p. 1409.1556. [Google Scholar]
  6. He, K., et al., Deep Residual Learning for Image Recognition. 2015(12). [Google Scholar]
  7. Xie, S., et al., Aggregated Residual Transformations for Deep Neural Networks. arXiv: 1611.05431v2, 2017. 2(4). [Google Scholar]
  8. J, L.S.S.C., Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. IEEE Computer Society Conference on Computer Vision & Pattern Recognition, 2006: p. 2006, 2:2169–2178. [Google Scholar]
  9. He, K., et al., Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. IEEE Trans Pattern Anal Mach Intell, 2015. 37(9): p. 1904–16. [CrossRef] [Google Scholar]
  10. Rastegari, M., et al., XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. 2016: p. 525–542. [Google Scholar]
  11. Sabour, S., N. Frosst and G.E. Hinton, Dynamic Routing Between Capsules. 2017(11). [Google Scholar]
  12. Zhang, C., J. Cheng and Q. Tian, Image-level classification by hierarchical structure learning with visual and semantic similarities. Information Sciences, 2018. 422(9): p. 271–281. [CrossRef] [Google Scholar]
  13. Zheng, P., et al., A set-level joint sparse representation for image set classification. Information Sciences, 2018. 448–449(2): p. 75–90. [Google Scholar]
  14. Mai, S.D. and L.T. Ngo, Multiple kernel approach to semi-supervised fuzzy clustering algorithm for land-cover classification. Engineering Applications of Artificial Intelligence, 2018. 68: p. 205–213. [CrossRef] [Google Scholar]
  15. Song Han, H.M.E.G., DSD: DENSE-SPARSE-DENSE TRAINING FOR DEEP NEURAL NETWORKS. 2017 (2). [Google Scholar]
  16. Ma, F. and S. Karaman, Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image. 2017. [Google Scholar]
  17. Akhtar, N., F. Shafait and A. Mian, Efficient classification with sparsity augmented collaborative representation. Pattern Recognition, 2017. 65: p. 136–145. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.