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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
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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
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
Leandro Giusti Mugnaini
Carolina Tavares Duarte
Anna H. Reali Costa
Artur Jordao
66
0
0
21 Nov 2024
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
Jinuk Kim
Marwa El Halabi
Mingi Ji
Hyun Oh Song
37
0
0
18 Jun 2024
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
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
Victor Quétu
Zhu Liao
Enzo Tartaglione
38
4
0
27 Apr 2024
NEPENTHE: Entropy-Based Pruning as a Neural Network Depth's Reducer
Zhu Liao
Victor Quétu
Van-Tam Nguyen
Enzo Tartaglione
39
2
0
24 Apr 2024
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
Ji Liu
Dehua Tang
Yuanxian Huang
Li Lyna Zhang
Xiaocheng Zeng
...
Jinzhang Peng
Yu-Chiang Frank Wang
Fan Jiang
Lu Tian
Ashish Sirasao
ViT
22
7
0
12 Jan 2024
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
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
Christian H. X. Ali Mehmeti-Göpel
Jan Disselhoff
8
5
0
30 Nov 2022
RepVGG: Making VGG-style ConvNets Great Again
Xiaohan Ding
X. Zhang
Ningning Ma
Jungong Han
Guiguang Ding
Jian-jun Sun
122
1,544
0
11 Jan 2021
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
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
Barret Zoph
Quoc V. Le
264
5,326
0
05 Nov 2016
Benefits of depth in neural networks
Matus Telgarsky
123
602
0
14 Feb 2016
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