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Network pruning reduces the computation costs of an over-parameterized network without performance damage.
Optimal brain damage
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
S. Han, H. Mao, and W. J. Dally · 2015
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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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Spatial pyramid pooling in deep convolutional networks for visual recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Net2net: Accelerating learning via knowledge transfer
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EIE: efficient inference engine on compressed deep neural network
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Deep residual learning for image recognition
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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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More is less: A more complicated network with less inference complexity
X. Dong, J. Huang, Y. Yang, and S. Yan · 2017
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Spatially adaptive computation time for residual networks
M. Figurnov, M. D. Collins, Y. Zhu, L. Zhang, J. Huang, D. Vetrov, and R. Salakhutdinov · 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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Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
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Quantized neural networks: Training neural networks with low precision weights and activations
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio · 2017
AMC: Automl for model compression and acceleration on mobile devices
J. L. Z. L. H. W. L.-J. L. He, Yihui and S. Han · 2018
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Soft filter pruning for accelerating deep convolutional neural networks
Y. He, G. Kang, X. Dong, Y. Fu, and Y. Yang · 2018
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Rethinking the value of network pruning
Z. Liu, M. Sun, T. Zhou, G. Huang, and T. Darrell · 2018
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Learning sparse neural networks through
C. Louizos, M. Welling, and D. P. Kingma · 2018
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Efficient neural architecture search via parameter sharing
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Learning sparse neural networks via sensitivity-driven regularization
E. Tartaglione, S. Lepsøy, A. Fiandrotti, and G. Francini · 2018
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Categorical reparameterization with gumbel-softmax
E. Jang, S. Gu, and B. Poole · 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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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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SGDR: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2017
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The concrete distribution: A continuous relaxation of discrete random variables
C. J. Maddison, A. Mnih, and Y. W. Teh · 2017
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A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
J. Yim, D. Joo, J. Bae, and J. Kim · 2017
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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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An empirical study of binary neural networks’ optimisation
M. Alizadeh, J. Fernández-Marqués, N. D. Lane, and Y. Gal · 2019
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ProxylessNAS: Direct neural architecture search on target task and hardware
H. Cai, L. Zhu, and S. Han · 2019
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Searching for a robust neural architecture in four gpu hours
X. Dong and Y. Yang · 2019
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Pruning filter via geometric median for deep convolutional neural networks acceleration
Y. He, P. Liu, Z. Wang, and Y. Yang · 2019
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Darts: Differentiable architecture search
H. Liu, K. Simonyan, and Y. Yang · 2019
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Cascaded projection: End-to-end network compression and acceleration
B. Minnehan and A. Savakis · 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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Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2019
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Network slimming by slimmable networks: Towards one-shot architecture search for channel numbers
J. Yu and T. Huang · 2019
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