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Updated: Jan 18, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Dynamic network compression via probabilistic channel pruning.

Kwanhee Lee1, Hyang-Won Lee1

  • 1Dept. of CSE, Konkuk University, Seoul, 05029, Republic of Korea.

Neural Networks : the Official Journal of the International Neural Network Society
|September 9, 2025
PubMed
Summary

This study introduces a novel probability-based connectivity module for efficient neural network compression. The method achieves significant parameter reduction and accuracy improvements without requiring fine-tuning, offering a practical solution for deep learning models.

Keywords:
ConnectivityModel compressionNeural network pruningProbabilistic channel pruning

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Last Updated: Jan 18, 2026

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Area of Science:

  • Artificial Intelligence
  • Computer Science

Background:

  • Deep learning models are computationally intensive, limiting their deployment.
  • Existing network pruning methods often lack practical speedups and require extensive fine-tuning.

Purpose of the Study:

  • To develop an efficient neural network compression technique that avoids fine-tuning overhead.
  • To introduce a probability-based connectivity module for dynamic channel activation and deactivation.

Main Methods:

  • Developed a probability-based connectivity module to determine channel connections dynamically during training.
  • Utilized convolution decomposition with the connectivity module and depth-wise convolution to induce sparsity.
  • Introduced resource-aware regularization for controlled compression levels.

Main Results:

  • Achieved significant parameter reduction (52.76% in ResNet-56, 46.05% in VGG-19) with accuracy boosts.
  • Demonstrated comparable compression and accuracy to state-of-the-art pruning methods.
  • Eliminated the need for fine-tuning pruned models.

Conclusions:

  • The proposed probability-based connectivity module offers an effective approach to neural network compression.
  • This method provides a practical and efficient solution for deploying deep learning models with reduced computational requirements.
  • The technique achieves high compression rates while maintaining or improving model accuracy.