Analysis of feature maps selection in supervised learning using convolutional neural networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8436 LNAI, Issue , 2014, Pages 59-70

  Chu, Joseph Lin ^a   Krzyzak, Adam ^a  

^a Gina Cody School of Engineering and Computer Science   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

NETWORK ARCHITECTURE; NETWORK LAYERS; OBJECT RECOGNITION;

BIOLOGICALLY INSPIRED; COMPUTATIONAL RESOURCES; CONVOLUTIONAL KERNEL; CONVOLUTIONAL NEURAL NETWORK; EMPIRICAL DATA; RECEPTIVE FIELD SIZES; RECEPTIVE FIELDS; THEORETICAL METHODS;

NEURAL NETWORKS;

EID: 84901663871     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-06483-3_6     Document Type: Conference Paper

References (16)

1. Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., Bengio, Y.: Theano: A CPU and GPU math expression compiler. In: Proceedings of the Python for Scientific Computing Conference, June 2010, SciPy (2010) oral Presentation
2. Boureau, Y.L., Ponce, J., LeCun, Y.: A theoretical analysis of feature pooling in visual recognition. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 111-118 (2010)
3. Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3642-3649. IEEE (2012)
4. Coates, A., Ng, A.Y., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In: International Conference on Artificial Intelligence and Statistics, pp. 215-223 (2011)
5. Eigen, D., Rolfe, J., Fergus, R., LeCun, Y.: Understanding Deep Architectures using a Recursive Convolutional Network. ArXiv e-prints (December 2013)
6. Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In: IEEE CVPR 2004, Workshop on Generative-Model Based Vision (2004)
7. Fukushima, K., Miyake, S.: Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recognition 15(6), 455-469 (1982)
8. Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in a cats visual cortex. Journal of Physiology (London) 160, 106-154 (1962)
9. Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25, 1106-1114 (2012)
10. LeCun, Y., Bottou, L., Bengio, Y.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278-2324 (1998)
11. LeCun, Y., Huang, F., Bottou, L.: Learning methods for generic object recognition with invariance to pose and lighting. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 97-104 (2004)
12. Nguyen, G.H., Phung, S.L., Bouzerdoum, A.: Reduced training of convolutional neural networks for pedestrian detection. In: International Conference on Information Technology and Applications (2009)
13. Scherer, D., Schulz, H., Behnke, S.: Accelerating large-scale convolutional neural networks with parallel graphics multiprocessors. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010, Part III. LNCS, vol. 6354, pp. 82-91. Springer, Heidelberg (2010)
14. Simard, P., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: ICDAR, vol. 3, pp. 958-962 (2003)
15. Uetz, R., Behnke, S.: Large-scale object recognition with cuda-accelerated hierarchical neural networks. In: IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009, vol. 1, pp. 536-541. IEEE (2009)
16. Uetz, R., Behnke, S.: Locally-connected hierarchical neural networks for gpuaccelerated object recognition. In: NIPS 2009 Workshop on Large-Scale Machine Learning: Parallelism and Massive Datasets (2009)