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Separable integral neural networks.

Jinhua Lin¹, Xin Li¹, Lin Ma²

¹Department of Computer Application Technology, Changchun University of Technology, PR China.

Neural Networks : the Official Journal of the International Neural Network Society

|November 30, 2024

Summary

This summary is machine-generated.

This study introduces separable integral neural networks (SINNs) for mobile devices, offering reduced computational costs and parameter counts compared to existing integral neural networks while maintaining competitive performance on image recognition tasks.

Keywords:

Convolutional neural networks Deep learning Integral neural networks

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Area of Science:

Deep Learning
Computer Vision
Neural Network Architectures

Background:

Integral neural networks (INNs) utilize continuous integral operators, but struggle to represent separable convolutions common in mobile applications.
Separable convolutions are crucial for efficient deep learning on resource-constrained devices.

Purpose of the Study:

To develop a novel neural network architecture capable of representing separable convolutions in a continuous manner.
To create lightweight separable integral neural networks (SINNs) suitable for mobile devices.

Main Methods:

Proposed a separable integral layer combining depth-wise and point-wise integral operators.
Designed five types of separable integral blocks (SIBs) based on classical CNN architectures.
Constructed and deployed SINNs on resource-constrained mobile devices.

Main Results:

SINNs demonstrate comparable performance to state-of-the-art INNs on the ImageNet dataset.
Achieved up to a 1/1.79 times reduction in computational cost compared to INNs.
SINNs using a ResNet101 backbone had 1.74x fewer parameters than INNs.

Conclusions:

SINNs effectively represent separable convolutions in a continuous framework.
The proposed architecture offers a compelling balance of performance and computational efficiency for mobile deep learning.
SINNs inherit the structural pruning benefits of INNs and the efficiency of separable convolutions.