Column Row Convolutional Neural Network: Reducing Parameters for Efficient Image Processing.
Neural Comput
; 36(4): 744-758, 2024 Mar 21.
Article
en En
| MEDLINE
| ID: mdl-38457753
ABSTRACT
Recent advancements in deep learning have achieved significant progress by increasing the number of parameters in a given model. However, this comes at the cost of computing resources, prompting researchers to explore model compression techniques that reduce the number of parameters while maintaining or even improving performance. Convolutional neural networks (CNN) have been recognized as more efficient and effective than fully connected (FC) networks. We propose a column row convolutional neural network (CRCNN) in this letter that applies 1D convolution to image data, significantly reducing the number of learning parameters and operational steps. The CRCNN uses column and row local receptive fields to perform data abstraction, concatenating each direction's feature before connecting it to an FC layer. Experimental results demonstrate that the CRCNN maintains comparable accuracy while reducing the number of parameters and compared to prior work. Moreover, the CRCNN is employed for one-class anomaly detection, demonstrating its feasibility for various applications.
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Colección:
01-internacional
Banco de datos:
MEDLINE
Idioma:
En
Revista:
Neural Comput
Asunto de la revista:
INFORMATICA MEDICA
Año:
2024
Tipo del documento:
Article