Fetching the paper…
Reading the bibliography…
Feature pooling layers (e.g., max pooling) in convolutional neural networks (CNNs) serve the dual purpose of providing increasingly abstract representations as well as yielding computational savings in subsequent convolutional layers.
Communication in the presence of noise
C. E. Shannon · 1949
Earlier work this paper cites.
Receptive fields, binocular interaction and functional architecture in the cats visual cortex
D. Hubel and T. Wiesel · 1962
Earlier work this paper cites.
Sampling theory in Fourier and signal analysis: foundations
J. R. Higgins · 1996
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Best practices for convolutional neural networks applied to visual document analysis
P. Y. Simard, D. Steinkraus, and J. C. Platt · 2003
Earlier work this paper cites.
Visual categorization with bags of keypoints
G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray · 2004
Earlier work this paper cites.
Pyramid match kernels: Discriminative classification with sets of image features
K. Grauman and T. Darrell · 2005
Earlier work this paper cites.
Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories
S. Lazebnik, C. Schmid, and J. Ponce · 2006
Earlier work this paper cites.
Deconvolutional networks
M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus · 2010
Earlier work this paper cites.
Selecting receptive fields in deep networks
A. Coates and A. Y. Ng · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
Adadelta: an adaptive learning rate method
M. D. Zeiler · 2012
Cited alongside, same era.
Maxout networks
I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio · 2013
Cited alongside, same era.
Stochastic pooling for regularization of deep convolutional neural networks
M. D. Zeiler and R. Fergus · 2013
Cited alongside, same era.
Discriminative unsupervised feature learning with convolutional neural networks
A. Dosovitskiy, J. T. Springenberg, M. Riedmiller, and T. Brox · 2014
Cited alongside, same era.
Improving deep neural networks with probabilistic maxout units
J. T. Springenberg and M. Riedmiller · 2014
Later among the works it cites.
Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Later among the works it cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Later among the works it cites.
Deep multi-patch aggregation network for image style, aesthetics, and quality estimation
X. Lu, Z. Lin, X. Shen, R. Mech, and J. Wang · 2015
Later among the works it cites.
Task-driven feature pooling for image classification
G. Xie, X. Zhang, X. Shu, S. Yan, and C. Liu · 2015
Later among the works it cites.
Deep representation learning with target coding
S. Yang, P. Luo, C. C. Loy, K. W. Shum, and X. Tang · 2015
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
B. Graham · 2014
Cited alongside, same era.
Learned-norm pooling for deep feedforward and recurrent neural networks
C. Gulcehre, K. Cho, R. Pascanu, and Y. Bengio · 2014
Cited alongside, same era.
Spatial pyramid pooling in deep convolutional networks for visual recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2014
Cited alongside, same era.
Network in network
M. Lin, Q. Chen, and S. Yan · 2014
Cited alongside, same era.
Later among the works it cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Closest in time.
Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree
C. Lee, P. Gallagher, and Z. Tu · 2016
Closest in time.
Stacked what-where auto-encoders
J. Zhao, M. Mathieu, R. Goroshin, and Y. Lecun · 2016
Closest in time.