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Enhancing convolutional neural network generalizability via low‐rank weight approximation

IET Image Processing(2025)10.1049/ipr2.13205Source: DataRank Database

Enhancing convolutional neural network generalizability via low‐rank weight approximation is a research paper published in IET Image Processing (2025). On theSindex it has a DataRank of 0.

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (4 grants)Impact (FWCI 0.00)OA: goldTopics (12)IDs (PubMed, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (3)

Shu Yang ORCID,Anru R. Zhang ORCID,Chenyin Gao ORCID

Live enrichment

PubMed:23304386 OpenAlex:https://openalex.org/W7117478024

National Science Foundation

Grant: CAREER‐2203741

National Science Foundation

Grant: SES 2242776

National Institutes of Health

Grant: 1R01AG066883

National Institutes of Health

Grant: 1R01ES031651

FWCI

0.00

Citation Percentile

0.5%

goldLicense: cc-by

journalhttps://doi.org/10.1049/ipr2.13205

publisherhttps://doi.org/10.1049/ipr2.13205

publisherhttps://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/ipr2.13205

Fields of Study

Image and Signal Denoising MethodsSparse and Compressive Sensing TechniquesAdvanced Image Fusion Techniques

Keywords

Generalizability theoryConvolutional neural networkPattern recognition (psychology)Image (mathematics)Noise reductionNoise (video)Set (abstract data type)Artificial neural networkImage segmentation

Sustainable Development Goals

SDG 0: Industry, innovation and infrastructure