.png){kind=icon}
Return
VCKNet: An adaptive, modular, and lightweight Variable-sized Convolutional Kernel Network
.png){kind=icon}
PRE
AI
DOI：10.1016/j.matcom.2026.07.049.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We introduce VCKNet (Variable-sized Convolutional Kernel Network), an adaptive, modular, and lightweight Convolutional Neural Network (CNN) with three kernel-scale branches, channel attention, and dynamic fusion. These components recalibrate scale-aware features to reduce redundancy and support discriminative learning. The proposed architectural design preserves computational efficiency while maintaining a clear structural organization. Experiments on standard benchmarks and in-the-wild datasets show that VCKNet effectively captures multiscale structure, achieving performance competitive with that of deeper state-of-the-art models. Statistical analysis across repeated runs further confirms VCKNet’s stability. Computational cost and deployment efficiency analyses further support its suitability for resource-constrained and production-oriented scenarios where flexibility, stability, and conceptual clarity are essential.
Keywords:
Multiscale image classification
Convolutional Neural Network
Variable-sized Convolutional Kernel
Multiscale feature representation
Pyramidal structure
Journal
IF:
4.4
Papers:
912
Citations:
1.0W
Organization
Cited Papers
ShareSaveShareSaveShareSaveEnd-to-end multi-scale residual network with parallel attention mechanism for fault diagnosis under noise and small samples.png)
ISA TRANSACTIONS
ISA TRANSACTIONSIF6.5
PREAI
ShareSaveShareSave
ShareSaveShareSave
View more