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Joint Multi-Dimension Pruning via Numerical Gradient Update.

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    This summary is machine-generated.

    Joint multi-dimension pruning (JointPruning) simultaneously optimizes spatial, depth, and channel aspects of neural networks. This collaborative approach achieves superior efficiency and performance compared to single-dimension pruning methods.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Neural network pruning is crucial for efficient model deployment.
    • Previous methods often focused on single dimensions (spatial, depth, or channel) independently.
    • Optimizing multiple pruning dimensions simultaneously presents a significant challenge.

    Purpose of the Study:

    • To introduce JointPruning, a novel method for simultaneous multi-dimension network pruning.
    • To develop a general framework for optimizing spatial, depth, and channel pruning concurrently.
    • To enhance model compression efficiency and performance through collaborative optimization.

    Main Methods:

    • Defined pruning as finding an optimal pruning vector (layer-wise channel number, spatial size, depth).
    • Developed a unique mapping from pruning vectors to pruned network structures.
    • Employed gradient optimization with self-adapted stochastic gradient estimation for efficient updates.
    • Implemented an end-to-end training process for collaborative optimization across dimensions.

    Main Results:

    • JointPruning outperforms single-dimension pruning strategies by optimizing collaboratively.
    • Achieved significant performance improvements over state-of-the-art methods on MobileNet V1&V2 under high compression ratios (2.5% and 2.6% margins).
    • Demonstrated effectiveness across various architectures (MobileNet V1-V3, ResNet) on the ImageNet dataset.

    Conclusions:

    • Joint multi-dimension pruning is an effective strategy for network compression.
    • The proposed JointPruning method offers superior efficiency and performance.
    • This approach enables significant model compression while maintaining high accuracy.