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Evolutionary multi-objective model compression for deep neural networks
Z. Wang
, T. Luo
,
Miqing Li
, J.T. Zhou
, R.S.M Goh
, Liangli Zhen
Computer Science
Research output
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Contribution to journal
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Article
›
peer-review
415
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Keyphrases
Evolutionary multi-objective
100%
Multi-objective Model
100%
Model Compression
100%
Deep Neural Network
100%
Energy Efficiency
60%
Energy Model
60%
Model Size
60%
Energy Consumption
40%
Pruning
40%
Edge Devices
40%
Face Recognition
20%
Significant Loss
20%
Diverse Populations
20%
Process Optimization
20%
Orthogonality
20%
Compression Rate
20%
Optimization Strategy
20%
Reduced Time
20%
Model Accuracy
20%
Coding Scheme
20%
Efficient Processing
20%
Language Translation
20%
Single Run
20%
Memory Consumption
20%
Population Evolution
20%
Weight Parameter
20%
VGG16
20%
Accuracy Loss
20%
Architecture Search
20%
CIFAR-10
20%
Network Pruning
20%
Dataflow Design
20%
Neural Network Quantization
20%
Computer Science
Multiobjective
100%
Objective Model
100%
Model Compression
100%
Deep Neural Network
100%
Energy Efficiency
60%
Energy Consumption
40%
Experimental Result
20%
Space Complexity
20%
Process Optimization
20%
Face Recognition
20%
Optimization Strategy
20%
VGG-16
20%
Compression Rate
20%
Translation (Languages)
20%
Engineering
Multi-Objective Model
100%
Deep Neural Network
100%
Energy Efficiency
60%
Experimental Result
20%
Broad Range
20%
Orthogonality
20%
Coding Scheme
20%
Optimization Strategy
20%