# Uncertainty-Aware Dual-Student Knowledge Distillation for Efficient Image Classification Source: [Uncertainty-Aware Dual-Student Knowledge Distillation for Efficient Image Classification]() Collection index: [llms.txt]() Data updated: 2025-11-26T05:06:16.949Z Authors: Aakash Gore, Anoushka Dey, Aryan Mishra Published: 2025-11-24T07:02:22.000Z ## Abstract Knowledge distillation has emerged as a powerful technique for model compression, enabling the transfer of knowledge from large teacher networks to compact student models. However, traditional knowledge distillation methods treat all teacher predictions equally, regardless of the teacher's confidence in those predictions. This paper proposes an uncertainty-aware dual-student knowledge distillation framework that leverages teacher prediction uncertainty to selectively guide student learning. We introduce a peer-learning mechanism where two heterogeneous student architectures, specifically ResNet-18 and MobileNetV2, learn collaboratively from both the teacher network and each other. Experimental results on ImageNet-100 demonstrate that our approach achieves superior performance compared to baseline knowledge distillation methods, with ResNet-18 achieving 83.84\\% top-1 accuracy and MobileNetV2 achieving 81.46\\% top-1 accuracy, representing improvements of 2.04\\% and 0.92\\% respectively over traditional single-student distillation approaches. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()