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Restructuring the Teacher and Student in Self-Distillation.

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    Restructured self-distillation (RSD) enhances model compression by restructuring teacher and student networks. This novel self-distillation framework improves performance by increasing architectural variety and enabling effective knowledge transfer.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Knowledge distillation is key for model compression, transferring knowledge from large to small models.
    • Self-distillation methods use a model's own knowledge but are limited by similar teacher-student architectures.
    • Existing self-distillation techniques struggle with architectural diversity, hindering optimal performance.

    Purpose of the Study:

    • To introduce a novel self-distillation framework, Restructured Self-Distillation (RSD), to enhance model compression.
    • To overcome the limitations of similar network architectures in self-distillation by increasing architectural variety.
    • To improve the effectiveness of knowledge transfer in self-distillation through architectural restructuring.

    Main Methods:

    • The proposed Restructured Self-Distillation (RSD) framework restructures both teacher and student networks.
    • The teacher model is expanded into a multi-branch topology, and diverse student sub-networks are created by discarding teacher branches.
    • A feature mixture block is randomly inserted to link teacher and student models, facilitating distillation in a mixed feature space. Teacher branches are converted back to the original structure to avoid extra inference costs.

    Main Results:

    • Comprehensive experiments demonstrate the effectiveness of the RSD framework across various architectures.
    • The proposed method shows significant improvements on standard datasets like CIFAR-10/100 and ImageNet.
    • RSD successfully enhances model compression and knowledge transfer compared to existing self-distillation methods.

    Conclusions:

    • Restructured Self-Distillation (RSD) offers a powerful new approach to model compression and self-distillation.
    • The framework's ability to increase architectural diversity is crucial for improving self-distillation performance.
    • RSD provides an effective solution for achieving high-performance model compression without increasing inference costs.