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Runtime Network Routing for Efficient Image Classification.

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    Runtime Network Routing (RNR) dynamically selects optimal paths in neural networks for efficient image classification. This approach outperforms static methods by adapting routing based on input images and available resources.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Static neural network acceleration methods often compromise network ability.
    • Dynamic routing offers potential for improved efficiency and adaptability in deep learning models.

    Purpose of the Study:

    • To propose a generic Runtime Network Routing (RNR) framework for efficient image classification.
    • To enable dynamic routing at runtime, adapting to input images and feature maps.

    Main Methods:

    • Modeling routing as a Markov decision process.
    • Utilizing reinforcement learning for training the routing agent.
    • Implementing a bottom-up, layer-by-layer routing strategy.

    Main Results:

    • RNR consistently outperforms static methods on CIFAR and ImageNet datasets.
    • Achieved improved performance at the same computational complexity compared to static methods.
    • Demonstrated adaptability to multi-path residual networks and incremental convolutional channel pruning.

    Conclusions:

    • RNR provides an efficient and adaptable framework for image classification.
    • The method preserves full network ability and allows adjustable resource balancing.
    • RNR is applicable to existing neural network structures and extendable to other applications.