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Previous works utilized ''smaller-norm-less-important'' criterion to prune filters with smaller norm values in a convolutional neural network.
Robust statistics on riemannian manifolds via the geometric median
P. T. Fletcher, S. Venkatasubramanian, and S. Joshi · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Runtime guarantees for regression problems
H. H. Chin, A. Madry, G. L. Miller, and R. Peng · 2013
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 · 2015
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.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
Earlier work this paper cites.
ImageNet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 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.
Geometric median in nearly linear time
M. B. Cohen, Y. T. Lee, G. Miller, J. Pachocki, and A. Sidford · 2016
Earlier work this paper cites.
Dynamic network surgery for efficient DNNs
Y. Guo, A. Yao, and Y. Chen · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Convolutional neural networks with low-rank regularization
C. Tai, T. Xiao, Y. Zhang, X. Wang, et al · 2016
Earlier work this paper cites.
Wide residual networks
S. Zagoruyko and N. Komodakis · 2016
Earlier work this paper cites.
Accelerating very deep convolutional networks for classification and detection
X. Zhang, J. Zou, K. He, and J. Sun · 2016
Earlier work this paper cites.
Learning to prune deep neural networks via layer-wise optimal brain surgeon
X. Dong, S. Chen, and S. Pan · 2017
Cited alongside, same era.
More is less: A more complicated network with less inference complexity
X. Dong, J. Huang, Y. Yang, and S. Yan · 2017
Cited alongside, same era.
Channel pruning for accelerating very deep neural networks
Y. He, X. Zhang, and J. Sun · 2017
Cited alongside, same era.
Pruning filters for efficient ConvNets
H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf · 2017
Cited alongside, same era.
Learning efficient convolutional networks through network slimming
Z. Liu, J. Li, Z. Shen, G. Huang, S. Yan, and C. Zhang · 2017
Cited alongside, same era.
ThiNet: A filter level pruning method for deep neural network compression
J.-H. Luo, J. Wu, and W. Lin · 2017
Cited alongside, same era.
Frequency-domain dynamic pruning for convolutional neural networks
Z. Liu, J. Xu, X. Peng, and R. Xiong · 2018
Closest in time.
Density estimation for statistics and data analysis
B. W. Silverman · 2018
Closest in time.
Clustering convolutional kernels to compress deep neural networks
S. Son, S. Nah, and K. Mu Lee · 2018
Closest in time.
Principal filter analysis for guided network compression
X. Suau, L. Zappella, V. Palakkode, and N. Apostoloff · 2018
Closest in time.
Clip-q: Deep network compression learning by in-parallel pruning-quantization
F. Tung and G. Mori · 2018
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Exploring linear relationship in feature map subspace for convnets compression
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Pruning convolutional neural networks for resource efficient transfer learning
P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Cited alongside, same era.
Incremental network quantization: Towards lossless cnns with low-precision weights
A. Zhou, A. Yao, Y. Guo, L. Xu, and Y. Chen · 2017
Cited alongside, same era.
Trained ternary quantization
C. Zhu, S. Han, H. Mao, and W. J. Dally · 2017
Cited alongside, same era.
“learning-compression” algorithms for neural net pruning
M. A. Carreira-Perpinán and Y. Idelbayev · 2018
Cited alongside, same era.
Coreset-based neural network compression
A. Dubey, M. Chatterjee, and N. Ahuja · 2018
Cited alongside, same era.
D. Wang, L. Zhou, X. Zhang, X. Bai, and J. Zhou · 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.
A systematic dnn weight pruning framework using alternating direction method of multipliers
T. Zhang, S. Ye, K. Zhang, J. Tang, W. Wen, M. Fardad, and Y. Wang · 2018
Closest in time.
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
Closest in time.
Scsp: Spectral clustering filter pruning with soft self-adaption manners
H. Zhuo, X. Qian, Y. Fu, H. Yang, and X. Xue · 2018
Closest in time.
Searching for a robust neural architecture in four gpu hours
X. Dong and Y. Yang · 2019
Closest in time.
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
Closest in time.
Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation
Y. Luo, L. Zheng, T. Guan, J. Yu, and Y. Yang · 2019
Closest in time.
Sim-real joint reinforcement transfer for 3d indoor navigation
F. Zhu, L. Zhu, and Y. Yang · 2019
Closest in time.