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Related Experiment Video

Updated: Nov 4, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

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CoDiNet: Path Distribution Modeling with Consistency and Diversity for Dynamic Routing.

Huanyu Wang, Zequn Qin, Songyuan Li

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 28, 2021
    PubMed
    Summary
    This summary is machine-generated.

    CoDiNet improves dynamic routing networks by mapping sample to routing spaces, ensuring consistent and diverse path distributions for better accuracy and efficiency in neural networks.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Dynamic routing networks enhance neural network accuracy and efficiency by optimizing path selection.
    • Existing methods often overlook the distribution of inference paths within the routing space.

    Purpose of the Study:

    • To propose CoDiNet, a novel dynamic routing method that models the relationship between sample and routing spaces.
    • To regularize routing path distributions for improved semantic consistency and diversity.

    Main Methods:

    • Formulating dynamic routing as a mapping from a sample space to a routing space.
    • Implementing CoDiNet to ensure semantically similar samples map to similar routing areas and dissimilar samples to different areas.
    • Designing a customizable dynamic routing module to balance accuracy and computational efficiency.

    Main Results:

    • CoDiNet achieves higher performance when integrated with ResNet models.
    • The method effectively reduces average computational cost across four benchmark datasets.
    • Demonstrates improved accuracy and efficiency in dynamic routing network applications.

    Conclusions:

    • CoDiNet offers a novel perspective on dynamic routing by focusing on space mapping and path distribution regularization.
    • The proposed method enhances neural network performance by optimizing routing path consistency and diversity.
    • CoDiNet provides a flexible solution for balancing accuracy and efficiency in deep learning models.