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Shift operation is an efficient alternative over depthwise separable convolution.
Optimal brain damage
Y. Lecun, J. S. Denker, and S. A. Solla · 1990
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Second order derivatives for network pruning: Optimal brain surgeon
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Learning multiple layers of features from tiny images
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Exploiting linear structure within convolutional networks for efficient evaluation
E. Denton, W. Zaremba, J. Bruna, Y. Lecun, and R. Fergus · 2014
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Compressing deep convolutional networks using vector quantization
Y. Gong, L. Liu, M. Yang, and L. D. Bourdev · 2014
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2014
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Speeding up convolutional neural networks with low rank expansions
M. Jaderberg, A. Vedaldi, and A. Zisserman · 2014
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Learning both weights and connections for efficient neural networks
S. Han, J. Pool, J. Tran, and W. J. Dally · 2015
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Speeding-up convolutional neural networks using fine-tuned cp-decomposition
V. Lebedev, Y. Ganin, M. Rakhuba, I. V. Oseledets, and V. S. Lempitsky · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, and M. Bernstein · 2015
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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
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Quantized neural networks: Training neural networks with low precision weights and activations
I. Hubara, M. Courbariaux, D. Soudry, R. Elyaniv, and Y. Bengio · 2016
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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 · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
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Deep model compression: Distilling knowledge from noisy teachers
B. B. Sau and V. N. Balasubramanian · 2016
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Convolutional neural networks with low-rank regularization
C. Tai, T. Xiao, Y. Zhang, X. Wang, and E. Weinan · 2016
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Learning structured sparsity in deep neural networks
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li · 2016
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Accelerating very deep convolutional networks for classification and detection
X. Zhang, J. Zou, K. He, and J. Sun · 2016
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Net-trim: Convex pruning of deep neural networks with performance guarantee
Coordinating filters for faster deep neural networks
W. Wen, C. Xu, C. Wu, Y. Wang, Y. Chen, and H. Li · 2017
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Interleaved group convolutions
T. Zhang, G. Qi, B. Xiao, and J. Wang · 2017
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Sqyeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
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Condensenet: An efficient densenet using learned group convolutions
S. Huang, Gao andLiu, L. V. Der Maaten, and K. Q. Weinberger · 2018
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DARTS: differentiable architecture search
H. Liu, K. Simonyan, and Y. Yang · 2018
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Shufflenet v2: Practical guidelines for efficient cnn architecture design
N. Ma, X. Zhang, H.-T. Zheng, and J. Sun · 2018
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A. Aghasi, N. Nguyen, and J. K. Romberg · 2017
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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
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Deep roots: Improving cnn efficiency with hierarchical filter groups
Y. Ioannou, D. Robertson, R. Cipolla, and A. Criminisi · 2017
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Pruning filters for efficient convnets
H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2017
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Progressive neural architecture search
C. Liu, M. Neumann, B. Zoph, J. Shlens, W. Hua, L. Li, L. Feifei, A. L. Yuille, J. Huang, and K. P. Murphy · 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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Mobilenetv2: Inverted residuals and linear bottlenecks
M. Sandler, A. G. Howard, M. Zhu, A. Zhmoginov, and L. Chen · 2018
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Igcv3: Interleaved low-rank group convolutions for efficient deep neural networks
K. Sun, M. Li, D. Liu, and J. Wang · 2018
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Shift: A zero flop, zero parameter alternative to spatial convolutions
B. Wu, A. Wan, X. Yue, P. H. Jin, S. Zhao, N. Golmant, A. Gholaminejad, J. E. Gonzalez, and K. Keutzer · 2018
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Interleaved structured sparse convolutional neural networks
G. Xie, J. Wang, T. Zhang, J. Lai, R. Hong, and G. Qi · 2018
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
X. Zhang, X. Zhou, M. Lin, and J. Sun · 2018
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A layer decomposition-recomposition framework for neuron pruning towards accurate lightweight networks
W. Chen, Y. Zhang, D. Xie, and S. Pu · 2019
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Constructing fast network through deconstruction of convolution
Y. Jeon and K. Junmo · 2019
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