Convolutional neural networks at constrained time cost

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 07-12-June-2015, Issue , 2015, Pages 5353-5360

  He, Kaiming ^a   Sun, Jian ^a  

^a MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BUDGET CONTROL; COMPLEX NETWORKS; COMPUTER VISION; CONVOLUTION; ECONOMIC AND SOCIAL EFFECTS; IMAGE RECOGNITION; NEURAL NETWORKS; PATTERN RECOGNITION; STATISTICAL TESTS;

BASELINE MODELS; CONVOLUTIONAL NEURAL NETWORK; FILTER SIZES; IN NETWORKS; RECOGNITION ACCURACY; TIME BUDGET; TIME COMPLEXITY; UNDER-CONSTRAINED;

NETWORK ARCHITECTURE;

EID: 84959197642     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7299173     Document Type: Conference Paper

References (24)

1. K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman. Return of the devil in the details: Delving deep into convolutional nets. In BMVC, 2014.
2. D. Ciresan, U. Meier, and J. Schmidhuber. Multi-column deep neural networks for image classification. In CVPR, 2012.
3. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009.
4. E. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus. Exploiting linear structure within convolutional networks for efficient evaluation. In NIPS, 2014.
5. D. Eigen, J. Rolfe, R. Fergus, and Y. LeCun. Understanding deep architectures using a recursive convolutional network. arXiv:1312.1847, 2013.
6. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014.
7. X. Glorot and Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. In International Conference on Artificial Intelligence and Statistics, pages 249-256, 2010.
8. B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik. Simultaneous detection and segmentation. In ECCV, pages 297-312, 2014.
9. K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. arXiv:1406.4729v2, 2014.
10. A. G. Howard. Some improvements on deep convolutional neural network based image classification. arXiv:1312.5402, 2013.
11. M. Jaderberg, A. Vedaldi, and A. Zisserman. Speeding up convolutional neural networks with low rank expansions. In BMVC, 2014.
12. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. arXiv:1408.5093, 2014.
13. A. Krizhevsky. One weird trick for parallelizing convolutional neural networks. arXiv:1404.5997, 2014.
14. A. Krizhevsky, I. Sutskever, and G. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
15. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural computation, 1989.
16. M. Lin, Q. Chen, and S. Yan. Network in network. arXiv:1312.4400, 2013.
17. F. Mamalet and C. Garcia. Simplifying convnets for fast learning. In Artificial Neural Networks and Machine Learning-ICANN, pages 58-65, 2012.
18. V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In ICML, pages 807-814, 2010.
19. A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson. Cnn features off-the-shelf: An astounding baseline for recogniton. In CVPR 2014, DeepVision Workshop, 2014.
20. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al. Imagenet large scale visual recognition challenge. arXiv:1409.0575, 2014.
21. P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Integrated recognition, localization and detection using convolutional networks. 2014.
22. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556, 2014.
23. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. arXiv:1409.4842, 2014.
24. M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional neural networks. In ECCV, 2014.