ReffAKD: A Machine Learning Method for Generating Soft Labels to Facilitate Knowledge Distillation in Student Models

ReffAKD is a machine learning method that helps train compact “student” models using a novel approach that generates high-quality soft labels without needing large teacher models or costly resources. This approach consistently outperforms traditional knowledge distillation methods while consuming fewer resources, making it practical for various applications beyond computer vision.

The value of ReffAKD lies in democratizing knowledge distillation, enabling researchers and practitioners in resource-constrained environments to benefit from this powerful technique with greater efficiency and accessibility.

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