Fetching the paper…
Reading the bibliography…
In this paper, we present a simple and modularized neural network architecture, named interleaved group convolutional neural networks (IGCNets).
80 million tiny images: A large data set for nonparametric object and scene recognition
A. Torralba, R. Fergus, and W. T. Freeman · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and F. Li · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Simplifying convnets for fast learning
F. Mamalet and C. Garcia · 2012
Earlier work this paper cites.
M. Lin, Q. Chen, and S. Yan · 2013
Earlier work this paper cites.
Exploiting linear structure within convolutional networks for efficient evaluation
E. L. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus · 2014
Earlier work this paper cites.
Speeding up convolutional neural networks with low rank expansions
M. Jaderberg, A. Vedaldi, and A. Zisserman · 2014
Earlier work this paper cites.
Flattened convolutional neural networks for feedforward acceleration
J. Jin, A. Dundar, and E. Culurciello · 2014
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio · 2014
Earlier work this paper cites.
Rigid-motion scattering for texture classification
L. Sifre and S. Mallat · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. A. Riedmiller · 2014
Earlier work this paper cites.
Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, T. Xiao, B. Xu, C. Zhang, and Z. Zhang · 2015
Earlier work this paper cites.
S. Han, H. Mao, and W. J. Dally · 2015
Cited alongside, same era.
Learning both weights and connections for efficient neural network
S. Han, J. Pool, J. Tran, and W. J. Dally · 2015
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
Training cnns with low-rank filters for efficient image classification
Y. Ioannou, D. P. Robertson, J. Shotton, R. Cipolla, and A. Criminisi · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Deep networks with stochastic depth
G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger · 2016
Later among the works it cites.
Deep roots: Improving CNN efficiency with hierarchical filter groups
Y. Ioannou, D. P. Robertson, R. Cipolla, and A. Criminisi · 2016
Later among the works it cites.
Fractalnet: Ultra-deep neural networks without residuals
G. Larsson, M. Maire, and G. Shakhnarovich · 2016
Later among the works it cites.
Pruning filters for efficient convnets
H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2016
Later among the works it cites.
Swapout: Learning an ensemble of deep architectures
S. Singh, D. Hoiem, and D. A. Forsyth · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Compression of deep convolutional neural networks for fast and low power mobile applications
Y. Kim, E. Park, S. Yoo, T. Choi, L. Yang, and D. Shin · 2015
Cited alongside, same era.
Deeply-supervised nets
C. Lee, S. Xie, P. W. Gallagher, Z. Zhang, and Z. Tu · 2015
Cited alongside, same era.
Training very deep networks
R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. E. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
M. Abdi and S. Nahavandi · 2016
Cited alongside, same era.
Xception: Deep learning with depthwise separable convolutions
F. Chollet · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Later among the works it cites.
Resnet in resnet: Generalizing residual architectures
S. Targ, D. Almeida, and K. Lyman · 2016
Later among the works it cites.
J. Wang, Z. Wei, T. Zhang, and W. Zeng · 2016
Later among the works it cites.
Learning structured sparsity in deep neural networks
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
Later among the works it cites.
Aggregated residual transformations for deep neural networks
S. Xie, R. B. Girshick, P. Dollár, Z. Tu, and K. He · 2016
Later among the works it cites.
S. Zagoruyko and N. Komodakis · 2016
Later among the works it cites.
On the connection of deep fusion to ensembling
L. Zhao, J. Wang, X. Li, Z. Tu, and W. Zeng · 2016
Later among the works it cites.
The power of sparsity in convolutional neural networks
S. Changpinyo, M. Sandler, and A. Zhmoginov · 2017
Closest in time.
Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi · 2017
Closest in time.