Pruning deep neural networks for green energy-efficient models: A survey
Over the past few years, larger and deeper neural network models, particularly convolutional
neural networks (CNNs), have consistently advanced state-of-the-art performance across …
neural networks (CNNs), have consistently advanced state-of-the-art performance across …
Literature review of deep network compression
Deep networks often possess a vast number of parameters, and their significant redundancy
in parameterization has become a widely-recognized property. This presents significant …
in parameterization has become a widely-recognized property. This presents significant …
[HTML][HTML] Pruning by explaining: A novel criterion for deep neural network pruning
The success of convolutional neural networks (CNNs) in various applications is
accompanied by a significant increase in computation and parameter storage costs. Recent …
accompanied by a significant increase in computation and parameter storage costs. Recent …
Automatic network pruning via hilbert-schmidt independence criterion lasso under information bottleneck principle
Most existing neural network pruning methods hand-crafted their importance criteria and
structures to prune. This constructs heavy and unintended dependencies on heuristics and …
structures to prune. This constructs heavy and unintended dependencies on heuristics and …
Automatic sparse connectivity learning for neural networks
Z Tang, L Luo, B **e, Y Zhu, R Zhao… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Since sparse neural networks usually contain many zero weights, these unnecessary
network connections can potentially be eliminated without degrading network performance …
network connections can potentially be eliminated without degrading network performance …
LwMLA-NET: A lightweight multi-level attention-based NETwork for segmentation of COVID-19 lungs abnormalities from CT images
COronaVIrus Disease 2019 (COVID-19) emerged as a global pandemic in the last two
years. Typical abnormal findings in chest computed tomography (CT) images of COVID-19 …
years. Typical abnormal findings in chest computed tomography (CT) images of COVID-19 …
Network pruning using sparse learning and genetic algorithm
Z Wang, F Li, G Shi, X **e, F Wang - Neurocomputing, 2020 - Elsevier
In recent years, convolutional neural networks (CNNs) have achieved success in the field of
computer vision. However, their large storage requirements and high computational …
computer vision. However, their large storage requirements and high computational …
Lightweight deep neural networks for ship target detection in SAR imagery
In recent years, deep convolutional neural networks (DCNNs) have been widely used in the
task of ship target detection in synthetic aperture radar (SAR) imagery. However, the vast …
task of ship target detection in synthetic aperture radar (SAR) imagery. However, the vast …
Filter pruning with a feature map entropy importance criterion for convolution neural networks compressing
J Wang, T Jiang, Z Cui, Z Cao - Neurocomputing, 2021 - Elsevier
Abstract Deep Neural Networks (DNN) has made significant progress in recent years.
However, its high computing and storage costs make it challenging to apply on resource …
However, its high computing and storage costs make it challenging to apply on resource …
Relevance-based channel selection in motor imagery brain–computer interface
Objective. Channel selection in the electroencephalogram (EEG)-based brain–computer
interface (BCI) has been extensively studied for over two decades, with the goal being to …
interface (BCI) has been extensively studied for over two decades, with the goal being to …