Feature flow regularization: Improving structured sparsity in deep neural networks

Yue Wu; Yuan Lan; Luchan Zhang; Yang Xiang

doi:10.1016/j.neunet.2023.02.013

ScienceGate Book Chapters

JOURNAL ARTICLE

Feature flow regularization: Improving structured sparsity in deep neural networks

Yue Wu Yuan Lan Luchan Zhang Yang Xiang

Year: 2023 Journal: Neural Networks Vol: 161 Pages: 598-613 Publisher: Elsevier BV

DOI: 10.1016/j.neunet.2023.02.013

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Regularization (linguistics) Pruning Feature (linguistics) Artificial neural network Artificial intelligence Filter (signal processing) Deep neural networks FLOPS Deep learning Pattern recognition (psychology) Algorithm Machine learning

Metrics

Cited By

1.46

FWCI (Field Weighted Citation Impact)

Refs

0.78

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Advanced Neural Network Applications

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Domain Adaptation and Few-Shot Learning

Physical Sciences → Computer Science → Artificial Intelligence

Human Pose and Action Recognition

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Feature flow regularization: Improving structured sparsity in deep neural networks

Metrics

Citation History

Topics

Related Documents

Structured Pruning for Deep Convolutional Neural Networks via Adaptive Sparsity Regularization

Improving Deep Neural Network Sparsity through Decorrelation Regularization

Deep Neural Networks Regularization Using a Combination of Sparsity Inducing Feature Selection Methods

Variance-Guided Structured Sparsity in Deep Neural Networks

Structured Sparsity of Convolutional Neural Networks via Nonconvex Sparse Group Regularization