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We study network pruning which aims to remove redundant channels/kernels and hence speed up the inference of deep networks.
A method for solving the convex programming problem with convergence rate o (1/kˆ 2)
Y. E. 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
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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
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Towards ultrahigh dimensional feature selection for big data
M. Tan, I. W. Tsang, and L. Wang · 2014
Earlier work this paper cites.
Gradient hard thresholding pursuit for sparsity-constrained optimization
X. Yuan, P. Li, and T. Zhang · 2014
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Learning both weights and connections for efficient neural network
S. Han, J. Pool, J. Tran, and W. Dally · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Deeply-supervised nets
C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu · 2015
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Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Structured transforms for small-footprint deep learning
V. Sindhwani, T. Sainath, and S. Kumar · 2015
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Training very deep networks
R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
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Deepid3: Face recognition with very deep neural networks
Y. Sun, D. Liang, X. Wang, and X. Tang · 2015
Earlier work this paper cites.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Earlier work this paper cites.
Matching pursuit lasso part i: Sparse recovery over big dictionary
M. Tan, I. W. Tsang, and L. Wang · 2015
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Learning the number of neurons in deep networks
J. M. Alvarez and M. Salzmann · 2016
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Dynamic network surgery for efficient dnns
Y. Guo, A. Yao, and Y. Chen · 2016
Earlier work this paper cites.
Ms-celeb-1m: A dataset and benchmark for large-scale face recognition
Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao · 2016
Earlier work this paper cites.
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
S. Han, H. Mao, and W. J. Dally · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
H. Hu, R. Peng, Y.-W. Tai, and C.-K. Tang · 2016
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Binarized neural networks
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
Cited alongside, same era.
The megaface benchmark: 1 million faces for recognition at scale
I. Kemelmacher-Shlizerman, S. M. Seitz, D. Miller, and E. Brossard · 2016
Cited alongside, same era.
Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
Cited alongside, same era.
Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
Cited alongside, same era.
You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
Cited alongside, same era.
Frontal to profile face verification in the wild
S. Sengupta, J.-C. Chen, C. Castillo, V. M. Patel, R. Chellappa, and D. W. Jacobs · 2016
Cited alongside, same era.
Amc: Automl for model compression and acceleration on mobile devices
Y. He, J. Lin, Z. Liu, H. Wang, L.-J. Li, and S. Han · 2018
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Efficient dnn neuron pruning by minimizing layer-wise nonlinear reconstruction error
C. Jiang, G. Li, C. Qian, and K. Tang · 2018
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Accelerating convolutional networks via global & dynamic filter pruning
S. Lin, R. Ji, Y. Li, Y. Wu, F. Huang, and B. Zhang · 2018
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Thinet: pruning cnn filters for a thinner net
J.-H. Luo, H. Zhang, H.-Y. Zhou, C.-W. Xie, J. Wu, and W. Lin · 2018
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Runtime network routing for efficient image classification
Y. Rao, J. Lu, J. Lin, and J. Zhou · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018
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Convolutional neural networks with low-rank regularization
C. Tai, T. Xiao, X. Wang, and W. E · 2016
Cited alongside, same era.
Temporal segment networks: Towards good practices for deep action recognition
L. Wang, Y. Xiong, Z. Wang, Y. Qiao, D. Lin, X. Tang, and L. Van Gool · 2016
Cited alongside, same era.
Learning structured sparsity in deep neural networks
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
Cited alongside, same era.
Accelerating very deep convolutional networks for classification and detection
X. Zhang, J. Zou, K. He, and J. Sun · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Structured pruning of deep convolutional neural networks
S. Anwar, K. Hwang, and W. Sung · 2017
Cited alongside, same era.
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Cosface: Large margin cosine loss for deep face recognition
H. Wang, Y. Wang, Z. Zhou, X. Ji, D. Gong, J. Zhou, Z. Li, and W. Liu · 2018
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Structured probabilistic pruning for convolutional neural network acceleration
H. Wang, Q. Zhang, Y. Wang, and H. Hu · 2018
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Netadapt: Platform-aware neural network adaptation for mobile applications
T.-J. Yang, A. Howard, B. Chen, X. Zhang, A. Go, M. Sandler, V. Sze, and H. Adam · 2018
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Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers
J. Ye, X. Lu, Z. Lin, and J. Z. Wang · 2018
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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
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Improving deep neural network sparsity through decorrelation regularization
X. Zhu, W. Zhou, and H. Li · 2018
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Towards effective low-bitwidth convolutional neural networks
B. Zhuang, C. Shen, M. Tan, L. Liu, and I. Reid · 2018
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Discrimination-aware channel pruning for deep neural networks
Z. Zhuang, M. Tan, B. Zhuang, J. Liu, Y. Guo, Q. Wu, J. Huang, and J. Zhu · 2018
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Arcface: Additive angular margin loss for deep face recognition
J. Deng, J. Guo, X. Niannan, and S. Zafeiriou · 2019
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Auto-embedding generative adversarial networks for high resolution image synthesis
Y. Guo, Q. Chen, J. Chen, Q. Wu, Q. Shi, and M. Tan · 2019
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Filter pruning via geometric median for deep convolutional neural networks acceleration
Y. He, P. Liu, Z. Wang, Z. Hu, and Y. Yang · 2019
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Learning to quantize deep networks by optimizing quantization intervals with task loss
S. Jung, C. Son, S. Lee, J. Son, J.-J. Han, Y. Kwak, S. J. Hwang, and C. Choi · 2019
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Snip: Single-shot network pruning based on connection sensitivity
N. Lee, T. Ajanthan, and P. H. Torr · 2019
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Toward compact convnets via structure-sparsity regularized filter pruning
S. Lin, R. Ji, Y. Li, C. Deng, and X. Li · 2019
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Towards optimal structured cnn pruning via generative adversarial learning
S. Lin, R. Ji, C. Yan, B. Zhang, L. Cao, Q. Ye, F. Huang, and D. Doermann · 2019
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Metapruning: Meta learning for automatic neural network channel pruning
Z. Liu, H. Mu, X. Zhang, Z. Guo, X. Yang, T. K.-T. Cheng, and J. Sun · 2019
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Rethinking the value of network pruning
Z. Liu, M. Sun, T. Zhou, G. Huang, and T. Darrell · 2019
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Importance estimation for neural network pruning
P. Molchanov, A. Mallya, S. Tyree, I. Frosio, and J. Kautz · 2019
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Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
M. Tan and Q. Le · 2019
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Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks
Z. You, K. Yan, J. Ye, M. Ma, and P. Wang · 2019
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Breaking winner-takes-all: Iterative-winners-out networks for weakly supervised temporal action localization
R. Zeng, C. Gan, P. Chen, W. Huang, Q. Wu, and M. Tan · 2019
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Structured binary neural networks for accurate image classification and semantic segmentation
B. Zhuang, C. Shen, M. Tan, L. Liu, and I. Reid · 2019
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Multi-way backpropagation for training compact deep neural networks
Y. Guo, J. Chen, Q. Du, A. Van Den Hengel, Q. Shi, and M. Tan · 2020
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