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    Area of Science:

    • Computer Vision
    • Deep Learning
    • Machine Learning

    Background:

    • Convolutional Neural Networks (CNNs) are powerful for computer vision but computationally expensive.
    • Existing compression methods often address convolutional or fully-connected layers separately, missing joint optimization opportunities.
    • Resource-limited environments like mobile devices face challenges deploying large CNN models.

    Purpose of the Study:

    • To introduce a holistic CNN compression framework, LRDKT, that jointly optimizes both convolutional and fully-connected layers.
    • To develop efficient methods for reducing model size and computational cost without significant accuracy loss.
    • To demonstrate the effectiveness of the proposed framework on benchmark datasets and transfer learning tasks.

    Main Methods:

    • Proposed a Low-Rank Decomposition (LRD) scheme with a closed-form solver to remove redundancies in convolutional kernels and fully-connected matrices.
    • Introduced a Knowledge Transfer (KT) based training scheme to recover accuracy loss and mitigate vanishing gradients by aligning student and teacher network responses.
    • Evaluated the framework on MNIST and ILSVRC 2012 benchmarks, including transfer learning tasks like domain adaptation and object detection.

    Main Results:

    • LRDKT achieved superior compression and speedup ratios compared to state-of-the-art methods on both MNIST and ILSVRC 2012 datasets.
    • The Knowledge Transfer scheme effectively recovered accuracy loss and improved the training of compressed models.
    • Demonstrated significant performance gains in transfer learning tasks, including domain adaptation and object detection.

    Conclusions:

    • The proposed LRDKT framework offers an effective holistic approach to CNN compression, addressing limitations of separate layer optimization.
    • LRDKT provides significant efficiency gains, making CNNs more deployable on resource-constrained devices.
    • The framework shows promise for improving performance in various computer vision applications through efficient model compression.