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Toward Efficient Convolutional Neural Networks With Structured Ternary Patterns.

Christos Kyrkou

    IEEE Transactions on Neural Networks and Learning Systems
    |April 23, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Structured Ternary Patterns (STePs) create efficient deep learning models by using static filters instead of learnable weights. This approach reduces resource demands for mobile and embedded applications, enhancing performance and lowering trainable parameters.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep learning (DL) models, particularly Convolutional Neural Networks (ConvNets), require substantial computational resources, limiting their deployment on resource-constrained devices.
    • Efficient DL models are crucial for on-device applications and reducing training costs.
    • Current ConvNet architectures often present challenges for mobile and embedded platforms due to high resource demands.

    Purpose of the Study:

    • To develop efficient ConvNet architectures by utilizing static convolutional filters derived from Local Binary Patterns (LBPs) and Haar features.
    • To introduce Structured Ternary Patterns (STePs) as a method for generating non-learnable filters during network initialization.
    • To reduce the number of trainable parameters and memory footprint in DL models.

    Main Methods:

    • Generated static convolutional filters, termed Structured Ternary Patterns (STePs), from LBPs and Haar features.
    • Initialized ConvNet architectures with STePs instead of learnable weight parameters.
    • Evaluated the proposed approach on four image classification datasets and for unmanned aerial vehicle (UAV)-based aerial vehicle detection.

    Main Results:

    • STePs significantly reduce trainable parameters by 40%-80% while maintaining high detection accuracy.
    • Common network backbones integrated with STePs demonstrate improved efficiency and competitive classification results.
    • Custom STeP-based networks offer favorable trade-offs for on-device applications.

    Conclusions:

    • Structured Ternary Patterns (STePs) provide an effective method for creating efficient DL architectures.
    • The use of non-learnable, pre-generated filters can substantially decrease model complexity and resource requirements.
    • This research encourages further exploration of prior-based non-learnable weights for enhancing DL model efficiency without post-training modifications.