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Channel pruning is one of the predominant approaches for deep model compression.
A method of solving a convex programming problem with convergence rate o (1/k2)
Y. Nesterov · 1983
Earlier work this paper cites.
Labeled faces in the wild: A database for studying face recognition in unconstrained environments
G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
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.
Greedy sparsity-constrained optimization
S. Bahmani, B. Raj, and P. T. Boufounos · 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.
Compressing deep convolutional networks using vector quantization
Y. Gong, L. Liu, M. Yang, and L. Bourdev · 2014
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 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.
Forward-backward greedy algorithms for general convex smooth functions over a cardinality constraint
J. Liu, J. Ye, and R. Fujimaki · 2014
Earlier work this paper cites.
Two-stream convolutional networks for action recognition in videos
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Deepface: Closing the gap to human-level performance in face verification
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf · 2014
Earlier work this paper cites.
Towards ultrahigh dimensional feature selection for big data
M. Tan, I. W. Tsang, and L. Wang · 2014
Earlier work this paper cites.
Learning face representation from scratch
D. Yi, Z. Lei, S. Liao, and S. Z. Li · 2014
Earlier work this paper cites.
Gradient hard thresholding pursuit for sparsity-constrained optimization
X. Yuan, P. Li, and T. Zhang · 2014
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
S. Han, J. Pool, J. Tran, and W. Dally · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Deeply-supervised nets
C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu · 2015
Cited alongside, same era.
Deep face recognition
O. M. Parkhi, A. Vedaldi, A. Zisserman, et al · 2015
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Cited alongside, same era.
Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
Cited alongside, same era.
Structured transforms for small-footprint deep learning
V. Sindhwani, T. Sainath, and S. Kumar · 2015
Cited alongside, same era.
Training very deep networks
R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
Cited alongside, same era.
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.
Accelerating very deep convolutional networks for classification and detection
X. Zhang, J. Zou, K. He, and J. Sun · 2016
Later among the works it cites.
Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
S. Zhou, Y. Wu, Z. Ni, X. Zhou, H. Wen, and Y. Zou · 2016
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Channel pruning for accelerating very deep neural networks
Y. He, X. Zhang, and J. Sun · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
Later among the works it cites.
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Y. Sun, D. Liang, X. Wang, and X. Tang · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
Matching pursuit lasso part i: Sparse recovery over big dictionary
M. Tan, I. W. Tsang, and L. Wang · 2015
Cited alongside, same era.
Learning the number of neurons in deep networks
J. M. Alvarez and M. Salzmann · 2016
Cited alongside, same era.
The shallow end: Empowering shallower deep-convolutional networks through auxiliary outputs
Y. Guo, M. Tan, Q. Wu, J. Chen, A. V. D. Hengel, and Q. Shi · 2016
Cited alongside, same era.
Dynamic network surgery for efficient dnns
Y. Guo, A. Yao, and Y. Chen · 2016
Cited alongside, same era.
Pruning filters for efficient convnets
H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2017
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Sphereface: Deep hypersphere embedding for face recognition
W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song · 2017
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Learning efficient convolutional networks through network slimming
Z. Liu, J. Li, Z. Shen, G. Huang, S. Yan, and C. Zhang · 2017
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Thinet: A filter level pruning method for deep neural network compression
J.-H. Luo, J. Wu, and W. Lin · 2017
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Pytorch: Tensors and dynamic neural networks in python with strong gpu acceleration, 2017
A. Paszke, S. Gross, S. Chintala, and G. Chanan · 2017
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Training sparse neural networks
S. Srinivas, A. Subramanya, and R. V. Babu · 2017
Later among the works it cites.
Incremental network quantization: Towards lossless cnns with low-precision weights
A. Zhou, A. Yao, Y. Guo, L. Xu, and Y. Chen · 2017
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Trained ternary quantization
C. Zhu, S. Han, H. Mao, and W. J. Dally · 2017
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Adversarial learning with local coordinate coding
J. Cao, Y. Guo, Q. Wu, C. Shen, J. Huang, and M. Tan · 2018
Closest in time.
Double forward propagation for memorized batch normalization
Y. Guo, Q. Wu, C. Deng, J. Chen, and M. Tan · 2018
Closest in time.
Mobilenetv2: Inverted residuals and linear bottlenecks
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018
Closest in time.
Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers
J. Ye, X. Lu, Z. Lin, and J. Z. Wang · 2018
Closest in time.
Nisp: Pruning networks using neuron importance score propagation
R. Yu, A. Li, C.-F. Chen, J.-H. Lai, V. I. Morariu, X. Han, M. Gao, C.-Y. Lin, and L. S. Davis · 2018
Closest in time.
Towards effective low-bitwidth convolutional neural networks
B. Zhuang, C. Shen, M. Tan, L. Liu, and I. Reid · 2018
Closest in time.