On vectorization of deep convolutional neural networks for vision tasks

Proceedings of the National Conference on Artificial Intelligence

Volumn 3, Issue , 2015, Pages 1840-1846

  Ren, Jimmy S ^a   Xu, Li ^a  

^a Lenovo Group   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CONVOLUTION; LEARNING SYSTEMS; NETWORK ARCHITECTURE; NEURAL NETWORKS; PERSONNEL TRAINING;

CONVOLUTIONAL NEURAL NETWORK; LOW-LEVEL VISION; PARALLEL IMPLEMENTATIONS; SPEED PERFORMANCE; STATE OF THE ART; TRAINING AND TESTING; VECTOR OPERATORS; VECTOR PROCESSING;

COMPUTER VISION;

EID: 84959922723     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. Bengio, Y., and LeCun, Y 2007. Scaling learning algorithms towards ai. Large-scale kernel machines 34:1-41.
2. Bergstra, J.; Breuleux, ().; Bastien, F.; Lamblin, P.; Pascanu, R.; Desjardins, G.; Turian, J.; Warde-Farley, D.; and Bengio, Y 2010. Theano: a cpu and gpu math compiler in python. In Python in Science Conf, 1-7.
3. Chellapilla, K.; Puri, S.; Simard, P.; et al. 2006. High perfor-mance convolutional neural networks for document processing. In International Workshop on Frontiers in Handwriting Recognition.
4. Coates, A.; Huval, B.; Wang, T.; Wu, D. J.; Catanzaro, B. C; and Ng, A. Y. 2013. Deep learning with COTS HPC systems. In ICML, 1337-1345.
5. Dean, J.; Corrado, G.; Monga, R.; Chen, K.; Devin, M.; Le, Q. V.; Mao, M. Z.; Ranzato, M.; Senior, A. W.; Tucker, P. A.; Yang, K.; and Ng, A. Y. 2012. Large scale distributed deep networks. In NIPS, 1232-1240.
6. Denton, E.; Zaremba, W.; Bruna, J.; LeCun, Y; and Fergus, R. 2014. Exploiting linear structure within convolutional networks for efficient evaluation. arXiv preprint arXiv: 1404.0736.
7. Eigen, D.; Krishnan, D.; and Fergus, R. 2013. Restoring an image taken through a window covered with dirt or rain. In NIPS.
8. Farabet, C; Poulet, C; Han, J. Y; and LeCun, Y 2009. Cnp: An fpga-based processor for convolutional networks. In International Conference on Field Programmable Logic and Applications, 32-37.
9. Hinton, G. E.; Srivastava, N.; Krizhevsky, A.; Sutskever, I.; and Salakhutdinov, R. R. 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.
10. Hinton, G.; Osindero, S.; and Teh, Y.-W. 2006. A fast learning algorithm for deep belief nets. Neural computation 18(7): 1527-1554.
11. Jackel, L.; Böser, B.; Denker, J.; Graf, H.; Le Cun, Y.; Guyon, I.; Henderson, D.; Howard, R.; Hubbard, W.; and Solla, S. 1990. Hardware requirements for neural-net optical character recognition. In International Joint Conference on Neural Networks, 855-861.
12. Jia, Y; Shelhamer, E.; Donahue, J.; Karayev, S.; Long, J.; Girshick, R.; Guadarrama, S.; and Darreil, T. 2014. Caffé: Convolutional architecture for fast feature embedding. arXiv preprint arXiv: 1408.5093.
13. Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012. Imagenet classification with deep convolutional neural networks. In NIPS, 1106-1114.
14. LeCun, Y; Boser, B.; Denker, J. S.; Henderson, D.; Howard, R. E.; Hubbard, W.; and Jackel, L. D. 1989. Backpropaga-tion applied to handwritten zip code recognition. Neural computation 1 (4):541-551.
15. LeCun, Y; Boser, B.; Denker, J. S.; Henderson, D.; Howard, R. E.; Hubbard, W.; and Jackel, L. D. 1990. Handwritten digit recognition with a back-propagation network. In NIPS.
16. LeCun, Y; Bottou, L.; Bengio, Y; and Haffner, P. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11):2278-2324.
17. Mathieu, M.; Henaff, M.; and LeCun, Y 2013. Fast training of convolutional networks through ffts. arXiv preprint arXiv: 1312.5851.
18. Nair, V., and Hinton, G. E. 2010. Rectified linear units improve restricted boltzmann machines. In ICML, 807-814.
19. Ragan-Kelley, J.; Barnes, C; Adams, A.; Paris, S.; Durand, F.; and Amarasinghe, S. 2013. Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines. ACM S1GPLAN Notices 48(6):519-530.
20. Russakovsky, O.; Deng, J.; Huang, Z.; Berg, A. C; and Fei-Fei, L. 2013. Detecting avocados to Zucchinis: What have we done, and where are we going? In ICCV, 2064-2071.
21. Sermanet, P.; Eigen, D.; Zhang, X.; Mathieu, M.; Fergus, R.; and LeCun, Y 2013. Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv: 1312.6229.
22. Simard, P. Y; Steinkraus, D.; and Piatt, J. C. 2003. Best practices for convolutional neural networks applied to visual document analysis. In International Conference on Document Analysis and Recognition, volume 2, 958-958.
23. Szegedy, C; Toshev, A.; and Erhan, D. 2013. Deep neural networks for object detection. In NIPS, 2553-2561.
24. Wan, L.; Zeiler, M. D.; Zhang, S.; LeCun, Y; and Fergus, R. 2013. Regularization of neural networks using dropconnect. In ICML, 1058-1066.
25. Xu, L.; Ren, J.; Liu, C; and Jia, J. 2014. Deep convolutional neural network for image deconvolution. In NIPS.