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We classify digits of real-world house numbers using convolutional neural networks (ConvNets).
A model of neuronal responses in visual area mt, 1997
E. P. Simoncelli and D. J. Heeger · 1997
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Digit classification on signboards for telephone number recognition
T. Yamaguchi, Y. Nakano, M. Maruyama, H. Miyao, and T. Hananoi · 2003
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Complex cell pooling and the statistics of natural images
A. Hyvärinen and U. Köster · 2005
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Character recognition in natural images
T. E. de Campos, B. R. Babu, and M. Varma · 2009
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Learning invariant features through topographic filter maps
K. Kavukcuoglu, M. Ranzato, R. Fergus, and Y. LeCun · 2009
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Eblearn: Open-source energy-based learning in c++
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Linear spatial pyramid matching using sparse coding for image classification
J. Yang, K. Yu, Y. Gong, and T. Huang · 2009
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A theoretical analysis of feature pooling in vision algorithms
Y. Boureau, J. Ponce, and Y. LeCun · 2010
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Human tracking using convolutional neural networks
J. Fan, W. Xu, Y. Wu, and Y. Gong · 2010
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A committee of neural networks for traffic sign classification
D. C. Ciresan, U. Meier, J. Masci, and J. Schmidhuber · 2011
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The MNIST database of handwritten digits
Y. Lecun and C. Cortes
Cited in the paper.
Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
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Traffic signs and pedestrians vision with multi-scale convolutional networks
P. Sermanet, K. Kavukcuoglu, and Y. LeCun · 2011
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Traffic sign recognition with multi-scale convolutional networks
P. Sermanet and Y. LeCun · 2011
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The German Traffic Sign Recognition Benchmark: A multi-class classification competition
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel · 2011
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