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Multiscale Hybrid Convolutional Deep Neural Networks with Channel Attention.

Hua Yang1, Ming Yang1, Bitao He2

  • 1Electrical Engineering College, Guizhou University, Guiyang 550025, China.

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|September 23, 2022
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Summary
This summary is machine-generated.

This study introduces a Mixture Attention (MA) module to enhance neural network performance while reducing computational complexity. The lightweight MA module achieves better accuracy and lower model cost, outperforming current state-of-the-art methods.

Keywords:
channel attentionconvolutional neural networksfeature fusionpyramid architectureskip connection

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Attention mechanisms enhance neural network performance but increase computational overhead.
  • Reducing model complexity while maintaining performance is a key research challenge.

Purpose of the Study:

  • To propose a lightweight Mixture Attention (MA) module for improving neural network efficiency and effectiveness.
  • To address the trade-off between performance gains and computational cost in attention networks.

Main Methods:

  • A multi-branch architecture processes input feature maps for multi-scale information extraction.
  • Group convolution is employed within each branch to minimize parameters.
  • Channel attention is applied to fused feature maps for statistical information extraction.

Main Results:

  • The MA module reduced network parameters by 9.86% and computational cost by 7.83%.
  • Top-1 accuracy improved by 1.99% compared to ResNet50.
  • Significant outperformance over state-of-the-art methods on CIFAR-10 and PASCAL-VOC benchmarks was demonstrated.

Conclusions:

  • The proposed Mixture Attention module offers an efficient and effective solution for enhancing neural networks.
  • MA achieves superior accuracy with reduced model complexity, making it suitable for resource-constrained applications.