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Biologically inspired, from the early HMAX model to Spatial Pyramid Matching, pooling has played an important role in visual recognition pipelines.
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
David H Hubel and Torsten N Wiesel · 1962
Earlier work this paper cites.
Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position
K. Fukushima and S. Miyake · 1982
Earlier work this paper cites.
Handwritten digit recognition with a back-propagation network
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1990
Earlier work this paper cites.
Efficient backprop
Y. LeCun, L. Bottou, G. Orr, and K. Müller · 1998
Earlier work this paper cites.
The structure of locally orderless images
J. J. Koenderink and A. J. Van Doorn · 1999
Earlier work this paper cites.
Neural Network for Pattern Recognition
C. M. Bishop · 1999
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Distinctive image features from scale-invariant keypoints
D. G. Lowe · 2004
Earlier work this paper cites.
Links between perceptrons, mlps and svms
R. Collobert and S. Bengio · 2004
Earlier work this paper cites.
Histograms of oriented gradients for human detection
N. Dalal and B. Triggs · 2005
Cited alongside, same era.
Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories
S. Lazebnik, C. Schmid, and J. Ponce · 2006
Cited alongside, same era.
Unsupervised learning of invariant feature hierarchies with applications to object recognition
M. A. Ranzato, F. J. Huang, Y. Boureau, and Y. LeCun · 2007
Cited alongside, same era.
80 million tiny images: A large data set for nonparametric object and scene recognition
A. Torralba, R. Fergus, and W. T. Freeman · 2008
Cited alongside, same era.
Hierarchical models of object recognition in cortex
M. Riesenhuber and T. Poggio · 2009
Cited alongside, same era.
Linear spatial pyramid matching using sparse coding for image classification
J. Yang, K. Yu, Y. Gong, and T. Huang · 2009
Beyond spatial pyramids: Receptive field learning for pooled image features
Y. Jia and C. Huang · 2011
Later among the works it cites.
Geometric lp-norm feature pooling for image classification
J. Feng, B. Ni, Q. Tian, and S. Yan · 2011
Later among the works it cites.
An analysis of single-layer networks in unsupervised feature learning
A. Coates, H. Lee, and A. Y. Ng · 2011
Later among the works it cites.
The importance of encoding versus training with sparse coding and vector quantization
A. Coates and A. Y. Ng · 2011
Later among the works it cites.
Beyond spatial pyramids: Receptive field learning for pooled image features
Y. Jia, C. Huang, and T. Darrell · 2012
Later among the works it cites.
Multi-column deep neural networks for image classification
D. Ciresan, U. Meier, and J. Schmidhuber · 2012
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Cited alongside, same era.
Modeling pixel means and covariances using factorized third-order boltzmann machines
M. A. Ranzato and G. E. Hinton · 2010
Cited alongside, same era.
Convolutional deep belief networks on cifar-10
A. Krizhevsky and G. Hinton · 2010
Cited alongside, same era.
Building high-level features using large scale unsupervised learning
Q. V. Le, M. A. Ranzato, R. Monga, M. Devin, K. Chen, G. S. Corrado, J. Dean, and A. Y. Ng
Cited in the paper.
Building high-level features using large scale unsupervised learning
Q. V. Le, R. Monga, M. Devin, G. Corrado, K. Chen, M. A. Ranzato, J. Dean, and A. Y. Ng
Cited in the paper.
Later among the works it cites.
I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio · 2013
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