August 4, 2025

Multi-Teacher Knowledge Distillation via Tucker-Guided Representation Alignment and Adaptive Feature Mapping

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The proposed framework enhances knowledge distillation through adaptive feature alignment and Tucker decomposition.
Experimental results show a significant accuracy improvement of 96.48% on CIFAR-100 and 91.70% on Tiny-ImageNet.
Multi-teacher supervision allows structured feature distillation using semantically rich representations from diverse sources.
An adaptive hybrid loss helps successfully transfer core tensor knowledge from teachers to the student network.

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Framework demonstrates improved accuracy in image classification, suggesting efficient multi-teacher supervision with adaptive alignment.

Sepahvand et al. (2025) studied this question.

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