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RED++ : Data-Free Pruning of Deep Neural Networks via Input Splitting
  and Output Merging

RED++ : Data-Free Pruning of Deep Neural Networks via Input Splitting and Output Merging

30 September 2021
Edouard Yvinec
Arnaud Dapogny
Matthieu Cord
Kévin Bailly
ArXivPDFHTML

Papers citing "RED++ : Data-Free Pruning of Deep Neural Networks via Input Splitting and Output Merging"

4 / 4 papers shown
Title
Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications
Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications
Vasileios Leon
Muhammad Abdullah Hanif
Giorgos Armeniakos
Xun Jiao
Muhammad Shafique
K. Pekmestzi
Dimitrios Soudris
29
3
0
20 Jul 2023
SCOP: Scientific Control for Reliable Neural Network Pruning
SCOP: Scientific Control for Reliable Neural Network Pruning
Yehui Tang
Yunhe Wang
Yixing Xu
Dacheng Tao
Chunjing Xu
Chao Xu
Chang Xu
AAML
44
166
0
21 Oct 2020
What is the State of Neural Network Pruning?
What is the State of Neural Network Pruning?
Davis W. Blalock
Jose Javier Gonzalez Ortiz
Jonathan Frankle
John Guttag
188
1,027
0
06 Mar 2020
Comparing Rewinding and Fine-tuning in Neural Network Pruning
Comparing Rewinding and Fine-tuning in Neural Network Pruning
Alex Renda
Jonathan Frankle
Michael Carbin
224
383
0
05 Mar 2020
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