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Improving Knowledge Distillation With a Customized Teacher.

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    This study introduces a new method for knowledge distillation (KD) to improve student model accuracy. By selecting teachers with more dispersed soft probabilities and using a novel pretraining strategy, customized teachers significantly enhance student performance.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Knowledge distillation (KD) transfers knowledge from large teacher networks to smaller student networks.
    • Current KD methods show performance variations even with similar teacher accuracies.
    • Teacher network's secondary soft probabilities influence student performance.

    Purpose of the Study:

    • To improve student model accuracy in knowledge distillation.
    • To identify and select more effective teacher networks.
    • To develop a novel KD approach using customized teachers.

    Main Methods:

    • Introduced standard deviation (σ) of secondary soft probabilities as a teacher selection indicator.
    • Proposed pretraining the teacher under dual supervision (PTDS) to disperse secondary soft probabilities.
    • Developed an asymmetrical transformation function (ATF) to further enhance probability dispersion.

    Main Results:

    • The proposed method, KDCT (Knowledge Distillation with a Customized Teacher), significantly improves student model accuracies.
    • PTDS and ATF effectively enhance the dispersion of teacher's secondary soft probabilities.
    • KDCT demonstrates effectiveness across image classification, transfer learning, and semantic segmentation tasks.

    Conclusions:

    • Teacher network's secondary soft probability dispersion is crucial for effective knowledge distillation.
    • KDCT offers a superior approach to teacher selection and pretraining for improved student performance.
    • The findings provide a novel strategy for optimizing knowledge distillation in computer vision.