Convolutional Neural Network Compression via Dynamic Parameter Rank Pruning

Convolutional Neural Network Compression via Dynamic Parameter Rank Pruning

While Convolutional Neural Networks (CNNs) excel at learning complex latent-space representations, their over-parameterization can lead to overfitting and reduced performance, particularly with limited data. This, alongside their high computational and memory demands, limits the applicability of CNN...

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Bibliographic Details
Main Authors: Manish Sharma, Jamison Heard, Eli Saber, Panagiotis Markopoulos
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Convolutional neural network
dynamic rank selection
image classification
low-rank factorization
model compression
model pruning
Online Access:https://ieeexplore.ieee.org/document/10851278/
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