Fetching the paper…
Reading the bibliography…
Neural network pruning reduces the computational cost of an over-parameterized network to improve its efficiency.
Discrete-variable extremum problems
G. Dantzig · 1957
Earlier work this paper cites.
On the solution of discrete programming problems
H. M. Markowitz and A. S. Manne · 1957
Earlier work this paper cites.
Optimal brain damage
Y. Lecun, J. Denker, and S. Solla · 1989
Earlier work this paper cites.
The rise and fall of knapsack cryptosystems
A. M. Odlyzko · 1990
Earlier work this paper cites.
Compressed sensing
D. L. Donoho · 2006
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 · 2009
Earlier work this paper cites.
Resource allocation on computational grids using a utility model and the knapsack problem
D. C. Vanderster, N. J. Dimopoulos, R. Parra-Hernandez, and R. J. Sobie · 2009
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.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 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.
Sparse convolutional neural networks
B. Liu, M. Wang, H. Foroosh, M. F. Tappen, and M. Pensky · 2015
Earlier work this paper cites.
Fitnets: Hints for thin deep nets
A. Romero, S. E. Kahou, P. Montréal, Y. Bengio, U. D. Montréal, A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio · 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.
Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
H. Hu, R. Peng, Y. Tai, and C. Tang · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Earlier work this paper cites.
Xception: Deep learning with depthwise separable convolutions
F. Chollet · 2017
Earlier work this paper cites.
More is less: A more complicated network with less inference complexity
X. Dong, J. Huang, Y. Yang, and S. Yan · 2017
Earlier work this paper cites.
Channel pruning for accelerating very deep neural networks
Y. He, X. Zhang, and J. Sun · 2017
Earlier work this paper cites.
Densely connected convolutional networks
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger · 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.
Pruning convolutional neural networks for resource efficient inference
P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz · 2017
Cited alongside, same era.
Aggregated residual transformations for deep neural networks
S. Xie, R. B. Girshick, P. Dollár, Z. Tu, and K. He · 2017
Cited alongside, same era.
Moonshine: Distilling with cheap convolutions
E. J. Crowley, G. Gray, and A. Storkey · 2018
Cited alongside, same era.
Global sparse momentum sgd for pruning very deep neural networks
X. Ding, X. Zhou, Y. Guo, J. Han, J. Liu, et al · 2019
Later among the works it cites.
Network pruning via transformable architecture search
X. Dong and Y. Yang · 2019
Later among the works it cites.
Bag of tricks for image classification with convolutional neural networks
T. He, Z. Zhang, H. Zhang, Z. Zhang, J. Xie, and M. Li · 2019
Later among the works it cites.
Filter pruning via geometric median for deep convolutional neural networks acceleration
Y. He, P. Liu, Z. Wang, Z. Hu, and Y. Yang · 2019
Later among the works it cites.
A comprehensive overhaul of feature distillation
B. Heo, J. Kim, S. Yun, H. Park, N. Kwak, and J. Y. Choi · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A pointer network based deep learning algorithm for 0–1 knapsack problem
S. Gu and T. Hao · 2018
Cited alongside, same era.
Soft filter pruning for accelerating deep convolutional neural networks
Y. He, G. Kang, X. Dong, Y. Fu, and Y. Yang · 2018
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
Cited alongside, same era.
Squeeze-and-excitation networks
J. Hu, L. Shen, and G. Sun · 2018
Cited alongside, same era.
Data-driven sparse structure selection for deep neural networks
Z. Huang and N. Wang · 2018
Cited alongside, same era.
Improvement of pruning method for convolution neural network compression
C. Liu and Q. Liu · 2018
Cited alongside, same era.
A. Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan, Q. V. Le, and H. Adam · 2019
Later among the works it cites.
Blockwisely supervised neural architecture search with knowledge distillation, 2019
C. Li, J. Peng, L. Yuan, G. Wang, X. Liang, L. Lin, and X. Chang · 2019
Later among the works it cites.
Application of neural network for the knapsack problem
D. Martini · 2019
Later among the works it cites.
Importance estimation for neural network pruning
P. Molchanov, A. Mallya, S. Tyree, I. Frosio, and J. Kautz · 2019
Later among the works it cites.
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, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
Later among the works it cites.
Collaborative channel pruning for deep networks
H. Peng, J. Wu, S. Chen, and J. Huang · 2019
Later among the works it cites.
EfficientNet: Rethinking model scaling for convolutional neural networks
M. Tan and Q. Le · 2019
Later among the works it cites.
Similarity-preserving knowledge distillation
F. Tung and G. Mori · 2019
Later among the works it cites.
Eca-net: Efficient channel attention for deep convolutional neural networks
Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, and Q. Hu · 2019
Later among the works it cites.
PyTorch image models repository, url:https://github.com/rwightman/pytorch-image-models
R. Wightman · 2019
Later among the works it cites.
Network slimming by slimmable networks: Towards one-shot architecture search for channel numbers
J. Yu and T. Huang · 2019
Later among the works it cites.
Slimmable neural networks
J. Yu, L. Yang, N. Xu, J. Yang, and T. Huang · 2019
Later among the works it cites.
Contrastive representation distillation
Y. Tian, D. Krishnan, and P. Isola · 2020
Closest in time.