*Result*: Reduced storage direct tensor ring decomposition for convolutional neural networks compression.

Title:
Reduced storage direct tensor ring decomposition for convolutional neural networks compression.
Authors:
Gabor M; Faculty of Electronics, Photonics, and Microsystems, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, Wroclaw, 50-370, Poland. Electronic address: mateusz.gabor@pwr.edu.pl., Zdunek R; Faculty of Electronics, Photonics, and Microsystems, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, Wroclaw, 50-370, Poland.
Source:
Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Jan; Vol. 193, pp. 107994. Date of Electronic Publication: 2025 Aug 27.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York : Pergamon Press, [c1988-
MeSH Terms:
Data Compression*/methods , Neural Networks, Computer*, Machine Learning ; Algorithms ; Humans ; Convolutional Neural Networks
Contributed Indexing:
Keywords: Convolutional neural networks; Reduced storage low-rank compression; Tensor ring decomposition
Entry Date(s):
Date Created: 20250904 Date Completed: 20251217 Latest Revision: 20251217
Update Code:
20260130
DOI:
10.1016/j.neunet.2025.107994
PMID:
40907363
Database:
MEDLINE

*Further Information*

*Convolutional neural networks (CNNs) are among the most widely used machine learning models for computer vision tasks, such as image classification. To improve the efficiency of CNNs, many compression approaches have been developed. Low-rank methods approximate the original convolutional kernel with a sequence of smaller convolutional kernels, leading to reduced storage and time complexities. In this study, we propose a novel low-rank CNN compression method that is based on reduced storage direct tensor ring decomposition (RSDTR). The proposed method offers a higher circular mode permutation flexibility, and it is characterized by large parameter and FLOPS compression rates, while preserving a good classification accuracy of the compressed network. The experiments, performed on the CIFAR-10 and ImageNet datasets, clearly demonstrate the efficiency of RSDTR in comparison to other state-of-the-art CNN compression approaches.
(Copyright © 2025 The Authors. Published by Elsevier Ltd.. All rights reserved.)*

*Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mateusz Gabor reports financial support was provided by National Science Centre Poland. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.*