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Layer Folding: Neural Network Depth Reduction using Activation
  Linearization

Layer Folding: Neural Network Depth Reduction using Activation Linearization

17 June 2021
Amir Ben Dror
Niv Zehngut
Avraham Raviv
E. Artyomov
Ran Vitek
R. Jevnisek
ArXivPDFHTML

Papers citing "Layer Folding: Neural Network Depth Reduction using Activation Linearization"

18 / 18 papers shown
Title
Temporal Action Detection Model Compression by Progressive Block Drop
Temporal Action Detection Model Compression by Progressive Block Drop
Xiaoyong Chen
Yong Guo
Jiaming Liang
Sitong Zhuang
Runhao Zeng
Xiping Hu
43
0
0
21 Mar 2025
Layer Pruning with Consensus: A Triple-Win Solution
Layer Pruning with Consensus: A Triple-Win Solution
Leandro Giusti Mugnaini
Carolina Tavares Duarte
Anna H. Reali Costa
Artur Jordao
71
0
0
21 Nov 2024
PReLU: Yet Another Single-Layer Solution to the XOR Problem
PReLU: Yet Another Single-Layer Solution to the XOR Problem
Rafael C. Pinto
Anderson R. Tavares
14
1
0
17 Sep 2024
LayerMerge: Neural Network Depth Compression through Layer Pruning and
  Merging
LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging
Jinuk Kim
Marwa El Halabi
Mingi Ji
Hyun Oh Song
37
0
0
18 Jun 2024
LaCoOT: Layer Collapse through Optimal Transport
LaCoOT: Layer Collapse through Optimal Transport
Victor Quétu
Nour Hezbri
Enzo Tartaglione
26
0
0
13 Jun 2024
A Generic Layer Pruning Method for Signal Modulation Recognition Deep
  Learning Models
A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Models
Yao Lu
Yutao Zhu
Yuqi Li
Dongwei Xu
Yun Lin
Qi Xuan
Xiaoniu Yang
23
5
0
12 Jun 2024
The Simpler The Better: An Entropy-Based Importance Metric To Reduce
  Neural Networks' Depth
The Simpler The Better: An Entropy-Based Importance Metric To Reduce Neural Networks' Depth
Victor Quétu
Zhu Liao
Enzo Tartaglione
38
4
0
27 Apr 2024
NEPENTHE: Entropy-Based Pruning as a Neural Network Depth's Reducer
NEPENTHE: Entropy-Based Pruning as a Neural Network Depth's Reducer
Zhu Liao
Victor Quétu
Van-Tam Nguyen
Enzo Tartaglione
41
2
0
24 Apr 2024
Revisiting Learning-based Video Motion Magnification for Real-time
  Processing
Revisiting Learning-based Video Motion Magnification for Real-time Processing
Hyunwoo Ha
Oh Hyun-Bin
Kim Jun-Seong
Byung-Ki Kwon
Kim Sung-Bin
Linh-Tam Tran
Ji-Yun Kim
Sung-Ho Bae
Tae-Hyun Oh
24
1
0
04 Mar 2024
UPDP: A Unified Progressive Depth Pruner for CNN and Vision Transformer
UPDP: A Unified Progressive Depth Pruner for CNN and Vision Transformer
Ji Liu
Dehua Tang
Yuanxian Huang
Li Lyna Zhang
Xiaocheng Zeng
...
Jinzhang Peng
Yu-Chiang Frank Wang
Fan Jiang
Lu Tian
Ashish Sirasao
ViT
24
7
0
12 Jan 2024
DeepReShape: Redesigning Neural Networks for Efficient Private Inference
DeepReShape: Redesigning Neural Networks for Efficient Private Inference
N. Jha
Brandon Reagen
28
10
0
20 Apr 2023
Enhancing the accuracies by performing pooling decisions adjacent to the
  output layer
Enhancing the accuracies by performing pooling decisions adjacent to the output layer
Yuval Meir
Yarden Tzach
Ronit D. Gross
Ofek Tevet
R. Vardi
Ido Kanter
29
5
0
10 Mar 2023
Nonlinear Advantage: Trained Networks Might Not Be As Complex as You
  Think
Nonlinear Advantage: Trained Networks Might Not Be As Complex as You Think
Christian H. X. Ali Mehmeti-Göpel
Jan Disselhoff
8
5
0
30 Nov 2022
RepVGG: Making VGG-style ConvNets Great Again
RepVGG: Making VGG-style ConvNets Great Again
Xiaohan Ding
X. Zhang
Ningning Ma
Jungong Han
Guiguang Ding
Jian-jun Sun
125
1,544
0
11 Jan 2021
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
183
1,027
0
06 Mar 2020
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision
  Applications
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard
Menglong Zhu
Bo Chen
Dmitry Kalenichenko
Weijun Wang
Tobias Weyand
M. Andreetto
Hartwig Adam
3DH
948
20,549
0
17 Apr 2017
Neural Architecture Search with Reinforcement Learning
Neural Architecture Search with Reinforcement Learning
Barret Zoph
Quoc V. Le
264
5,326
0
05 Nov 2016
Benefits of depth in neural networks
Benefits of depth in neural networks
Matus Telgarsky
123
602
0
14 Feb 2016
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