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Related Experiment Video

Updated: May 26, 2026

Author Spotlight: Analgesic Effect of Tuina on Rat Models with Compression of the Dorsal Root Ganglion Pain
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ONNXPruner: ONNX-Based General Model Pruning Adapter.

Dongdong Ren, Wenbin Li, Tianyu Ding

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 25, 2025
    PubMed
    Summary
    This summary is machine-generated.

    ONNXPruner enhances model pruning by adapting algorithms to ONNX models. Its node association trees and tree-level evaluation improve pruning efficacy across diverse platforms without extra operations.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Model pruning advancements focus on algorithms and benchmarks.
    • Practical application across diverse models and platforms is challenging.

    Purpose of the Study:

    • Introduce ONNXPruner, a versatile adapter for ONNX format models.
    • Streamline pruning adaptation across deep learning frameworks and hardware.

    Main Methods:

    • Utilize node association trees for automatic adaptation to model architectures.
    • Employ a tree-level evaluation method for comprehensive analysis.
    • Guide pruning by clarifying structural relationships and impact on interconnected nodes.

    Main Results:

    • ONNXPruner demonstrates strong adaptability across various models and datasets.
    • The tree-level evaluation enhances pruning performance without additional computational cost.
    • Node association trees effectively guide the pruning process.

    Conclusions:

    • ONNXPruner facilitates the practical application of model pruning.
    • The proposed methods advance the efficacy and adaptability of pruning techniques.
    • This work bridges the gap between pruning research and real-world deployment.