Fetching the paper…
Reading the bibliography…
We present a post-training weight pruning method for deep neural networks that achieves accuracy levels tolerable for the production setting and that is sufficiently fast to be run on commodity hardware such as desktop CPUs or edge devices.
Data-free parameter pruning for deep neural networks
S. Srinivas and R. V. Babu · 2015
Earlier work this paper cites.
To prune, or not to prune: exploring the efficacy of pruning for model compression
M. Zhu and S. Gupta · 2017
Earlier work this paper cites.
Post-training 4-bit quantization of convolution networks for rapid-deployment
R. Banner, Y. Nahshan, E. Hoffer, and D. Soudry · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Averaging weights leads to wider optima and better generalization
P. Izmailov, D. Podoprikhin, T. Garipov, D. Vetrov, and A. G. Wilson · 2018
Earlier work this paper cites.
The state of sparsity in deep neural networks
T. Gale, E. Elsen, and S. Hooker · 2019
Earlier work this paper cites.
Wootz: A compiler-based framework for fast cnn pruning via composability
H. Guan, X. Shen, and S.-H. Lim · 2019
Earlier work this paper cites.
Data-free quantization through weight equalization and bias correction
M. Nagel, M. v. Baalen, T. Blankevoort, and M. Welling · 2019
Earlier work this paper cites.
Lookahead optimizer: k steps forward, 1 step back
M. R. Zhang, J. Lucas, G. Hinton, and J. Ba · 2019
Cited alongside, same era.
What is the state of neural network pruning?
D. Blalock, J. J. G. Ortiz, J. Frankle, and J. Guttag · 2020
Cited alongside, same era.
Fast sparse convnets
E. Elsen, M. Dukhan, T. Gale, and K. Simonyan · 2020
Cited alongside, same era.
Accelerating sparse dnn models without hardware-support via tile-wise sparsity
C. Guo, B. Y. Hsueh, J. Leng, Y. Qiu, Y. Guan, Z. Wang, X. Jia, X. Li, M. Guo, and Y. Zhu · 2020
Cited alongside, same era.
Layer-wise data-free cnn compression
M. Horton, Y. Jin, A. Farhadi, and M. Rastegari · 2020
A deeper look at the layerwise sparsity of magnitude-based pruning
J. Lee, S. Park, S. Mo, S. Ahn, and J. Shin · 2020
Later among the works it cites.
Eagleeye: Fast sub-net evaluation for efficient neural network pruning
B. Li, B. Wu, J. Su, and G. Wang · 2020
Later among the works it cites.
Layerwise sparse coding for pruned deep neural networks with extreme compression ratio
X. Liu, W. Li, J. Huo, L. Yao, and Y. Gao · 2020
Later among the works it cites.
Up or down? adaptive rounding for post-training quantization
M. Nagel, R. A. Amjad, M. Van Baalen, C. Louizos, and T. Blankevoort · 2020
Later among the works it cites.
Woodfisher: Efficient second-order approximation for neural network compression
S. P. Singh and D. Alistarh · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Improving post training neural quantization: Layer-wise calibration and integer programming
I. Hubara, Y. Nahshan, Y. Hanani, R. Banner, and D. Soudry · 2020
Cited alongside, same era.
Pre-training without natural images
H. Kataoka, K. Okayasu, A. Matsumoto, E. Yamagata, R. Yamada, N. Inoue, A. Nakamura, and Y. Satoh · 2020
Cited alongside, same era.
Soft threshold weight reparameterization for learnable sparsity
A. Kusupati, V. Ramanujan, R. Somani, M. Wortsman, P. Jain, S. Kakade, and A. Farhadi · 2020
Cited alongside, same era.
Y. Li, R. Gong, X. Tan, Y. Yang, P. Hu, Q. Zhang, F. Yu, W. Wang, and S. Gu · 2021
Closest in time.
Can vision transformers learn without natural images?
K. Nakashima, H. Kataoka, A. Matsumoto, K. Iwata, and N. Inoue · 2021
Closest in time.