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S3Pool: Pooling with Stochastic Spatial Sampling

S3Pool: Pooling with Stochastic Spatial Sampling

16 November 2016
Shuangfei Zhai
Hui Wu
Abhishek Kumar
Yu Cheng
Y. Lu
Zhongfei Zhang
Rogerio Feris
ArXiv (abs)PDFHTMLGithub (7★)

Papers citing "S3Pool: Pooling with Stochastic Spatial Sampling"

13 / 13 papers shown
Title
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,322
0
10 Dec 2015
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed,
  Gated, and Tree
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree
Chen-Yu Lee
Patrick W. Gallagher
Zhuowen Tu
AI4CE
73
484
0
30 Sep 2015
Stacked What-Where Auto-encoders
Stacked What-Where Auto-encoders
Jiaqi Zhao
Michaël Mathieu
Ross Goroshin
Yann LeCun
DiffMBDL
62
258
0
08 Jun 2015
Batch Normalization: Accelerating Deep Network Training by Reducing
  Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe
Christian Szegedy
OOD
463
43,328
0
11 Feb 2015
Fractional Max-Pooling
Fractional Max-Pooling
Benjamin Graham
TPM
98
517
0
18 Dec 2014
Discriminative Unsupervised Feature Learning with Exemplar Convolutional
  Neural Networks
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
Alexey Dosovitskiy
Philipp Fischer
Jost Tobias Springenberg
Martin Riedmiller
Thomas Brox
OODSSL
87
1,022
0
26 Jun 2014
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual
  Recognition
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
ObjD
399
11,227
0
18 Jun 2014
Improving Deep Neural Networks with Probabilistic Maxout Units
Improving Deep Neural Networks with Probabilistic Maxout Units
Jost Tobias Springenberg
Martin Riedmiller
BDLOOD
210
101
0
20 Dec 2013
Network In Network
Network In Network
Min Lin
Qiang Chen
Shuicheng Yan
291
6,279
0
16 Dec 2013
Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks
Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks
Çağlar Gülçehre
Kyunghyun Cho
Razvan Pascanu
Yoshua Bengio
96
170
0
07 Nov 2013
Maxout Networks
Maxout Networks
Ian Goodfellow
David Warde-Farley
M. Berk Mirza
Aaron Courville
Yoshua Bengio
OOD
243
2,178
0
18 Feb 2013
Stochastic Pooling for Regularization of Deep Convolutional Neural
  Networks
Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
Matthew D. Zeiler
Rob Fergus
193
989
0
16 Jan 2013
ADADELTA: An Adaptive Learning Rate Method
ADADELTA: An Adaptive Learning Rate Method
Matthew D. Zeiler
ODL
155
6,625
0
22 Dec 2012
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