Convolutional neural network

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In computer science, a convolutional neural network is a type of feed-forward artificial neural network where the individual neurons are tiled in such a way that they respond to overlapping regions in the visual field.[1] Convolutional networks were inspired by biological processes[2] and are variations of multilayer perceptrons which are designed to use minimal amounts of preprocessing.[3] They are widely used models for image recognition.[4]


When used for image recognition, convolutional neural networks consist of multiple layers of small neuron collections which look at small portions of the input image, called receptive fields. The results of these collections are then tiled so that they overlap to obtain a better representation of the original image; this is repeated for every such layer. Because of this, they are able to tolerate translation of the input image.[4] Convolutional networks may include local or global pooling layers, which combine the outputs of neuron clusters.[5][6] They also consist of various combinations of convolutional layers and fully connected layers, with pointwise nonlinearity applied at the end of or after each layer.[7] One major advantage of convolutional networks is the use of shared weight in convolutional layers, which means that the same filter (weights bank) is used for each pixel in the layer; this both reduces required memory size and improves performance.[3]

Some time-delay neural networks also use a very similar architecture to convolutional neural networks, especially those for image recognition and/or classification tasks, since the "tiling" of the neuron outputs can easily be carried out in timed stages in a manner useful for analysis of images.[8]


Convolutional neural networks were introduced in a 1980 paper by Kunihiko Fukushima.[7][9] Their design was later improved in 1998 by Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner,[10] generalized in 2003 by Sven Behnke,[11] and simplified by Patrice Simard, David Steinkraus, and John C. Platt in the same year.[12] In 2006 several publications described new ways to train convolutional neural networks more efficiently that allowed for networks with more layers to be trained.[13][14][15] In 2011, they were refined by Dan Ciresan et al. and were implemented on a GPU with impressive performance results.[5] In 2012, Dan Ciresan et al. significantly improved upon the best performance in the literature for multiple image databases, including the MNIST database, the NORB database, the HWDB1.0 dataset (Chinese characters), and the CIFAR10 dataset (dataset of 60000 32x32 labeled RGB images).[7]


Convolutional neural networks are often used in image recognition systems. They have achieved an error rate of 0.23 percent on the MNIST database, which as of February 2012 is the lowest achieved on the database.[7] Another paper on using convolutional neural networks for image classification reported that the learning process of convolutional neural networks was "surprisingly fast"; in the same paper, the best published results at the time were achieved in the MNIST database and the NORB database.[5]

When applied to facial recognition, they were able to contribute to a large decrease in error rate.[16] In another paper, they were able to achieve a 97.6 percent recognition rate on "5,600 still images of more than 10 subjects".[2] Convolutional neural networks have been used to assess video quality in an objective way after being manually trained; the resulting system had a very low root mean square error.[8]

See also[edit]


  1. ^ "Convolutional Neural Networks (LeNet) - DeepLearning 0.1 documentation". DeepLearning 0.1. LISA Lab. Retrieved 31 August 2013. 
  2. ^ a b Matusugu, Masakazu; Katsuhiko Mori; Yusuke Mitari; Yuji Kaneda (2003). "Subject independent facial expression recognition with robust face detection using a convolutional neural network". Neural Networks 16 (5): 555–559. doi:10.1016/S0893-6080(03)00115-1. Retrieved 17 November 2013. 
  3. ^ a b LeCun, Yann. "LeNet-5, convolutional neural networks". Retrieved 16 November 2013. 
  4. ^ a b Korekado, Keisuke; Morie, Takashi; Nomura, Osamu; Ando, Hiroshi; Nakano, Teppei; Matsugu, Masakazu; Iwata, Atsushi (2003). "A Convolutional Neural Network VLSI for Image Recognition Using Merged/Mixed Analog-Digital Architecture". Knowledge-Based Intelligent Information and Engineering Systems: 169–176. CiteSeerX: 
  5. ^ a b c Ciresan, Dan; Ueli Meier; Jonathan Masci; Luca M. Gambardella; Jurgen Schmidhuber (2011). "Flexible, High Performance Convolutional Neural Networks for Image Classification". Proceedings of the Twenty-Second international joint conference on Artificial Intelligence-Volume Volume Two 2: 1237–1242. Retrieved 17 November 2013. 
  6. ^ Krizhevsky, Alex. "ImageNet Classification with Deep Convolutional Neural Networks". Retrieved 17 November 2013. 
  7. ^ a b c d Ciresan, Dan; Meier, Ueli; Schmidhuber, Jürgen (June 2012). "Multi-column deep neural networks for image classification". 2012 IEEE Conference on Computer Vision and Pattern Recognition (New York, NY: Institute of Electrical and Electronics Engineers (IEEE)): 3642–3649. arXiv:1202.2745v1. doi:10.1109/CVPR.2012.6248110. ISBN 9781467312264. OCLC 812295155. Retrieved 2013-12-09. 
  8. ^ a b Le Callet, Patrick; Christian Viard-Gaudin; Dominique Barba (2006). "A Convolutional Neural Network Approach for Objective Video Quality Assessment". IEEE Transactions on Neural Networks 17 (5): 1316–1327. doi:10.1109/TNN.2006.879766. PMID 17001990. Retrieved 17 November 2013. 
  9. ^ Fukushima, Kunihiko (1980). "Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position". Biological Cybernetics 36 (4): 193–202. doi:10.1007/BF00344251. PMID 7370364. Retrieved 16 November 2013. 
  10. ^ LeCun, Yann; Léon Bottou; Yoshua Bengio; Patrick Haffner (1998). "Gradient-based learning applied to document recognition". Proceedings of the IEEE 86 (11): 2278–2324. doi:10.1109/5.726791. Retrieved 16 November 2013. 
  11. ^ S. Behnke. Hierarchical Neural Networks for Image Interpretation, volume 2766 of Lecture Notes in Computer Science. Springer, 2003.
  12. ^ Simard, Patrice, David Steinkraus, and John C. Platt. "Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis." In ICDAR, vol. 3, pp. 958-962. 2003.
  13. ^ Hinton, GE; Osindero, S; Teh, YW (Jul 2006). "A fast learning algorithm for deep belief nets.". Neural computation 18 (7): 1527–54. PMID 16764513. 
  14. ^ Bengio, Yoshua; Lamblin, Pascal; Popovici, Dan; Larochelle, Hugo (2007). "Greedy Layer-Wise Training of Deep Networks". Advances in Neural Information Processing Systems: 153–160. 
  15. ^ Ranzato, MarcAurelio; Poultney, Christopher; Chopra, Sumit; LeCun, Yann (2007). "Efficient Learning of Sparse Representations with an Energy-Based Model". Advances in Neural Information Processing Systems. 
  16. ^ Lawrence, Steve; C. Lee Giles; Ah Chung Tsoi; Andrew D. Back (1997). "Face Recognition: A Convolutional Neural Network Approach". Neural Networks, IEEE Transactions on 8 (1): 98–113. doi:10.1109/72.554195. CiteSeerX: 

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