A unified DNN weight compression framework using reweighted optimization methods

A unified DNN weight compression framework using reweighted optimization methods

To address the large model sizes and intensive computation requirements of deep neural networks (DNNs), weight pruning techniques have been proposed and generally fall into two categories: static regularization-based pruning and dynamic regularization-based pruning. However, the static method often...

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Bibliographic Details
Main Authors: Mengchen Fan, Tianyun Zhang, Xiaolong Ma, Jiacheng Guo, Zheng Zhan, Shanglin Zhou, Minghai Qin, Caiwen Ding, Baocheng Geng, Makan Fardad, Yanzhi Wang
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Intelligent Systems with Applications
Subjects:
Deep neural networks
Weight pruning
Model compression
Adversarial training
Online Access:http://www.sciencedirect.com/science/article/pii/S2667305325000821
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