Student Capacity Moderates Knowledge Distillation Effectiveness: A Systematic Study Across ResNet Teacher-Student Pairs on CIFAR-10
arXiv:2605.31191v1 Announce Type: new Abstract: We investigate how teacher-student capacity relationships modulate knowledge distillation (KD) effectiveness in ResNet-based image classification on CIFAR-10. Across three teacher-student pairs -- R50->R18, R34->R18, and R50->R34 -- we compare Logit-KD and Feature-KD under controlled, reproducible conditions (3 seeds, mean+/-std reported throughout). We report three main findings. First, student capacity is a key moderating factor in distillation gain: R34 students benefit substantially more from KD than R18 students even when teacher-student acc
This research provides timely empirical insights into optimizing knowledge distillation strategies as AI models become more complex and efficiency in deployment is prioritized.
Understanding how student capacity moderates knowledge distillation effectiveness is crucial for developing more efficient and performant AI systems, especially for resource-constrained environments.
This research refines our understanding of knowledge distillation, shifting from a universal application to a nuanced approach where student model capacity significantly influences the method's efficacy.
- · AI researchers
- · ML engineers
- · Edge AI developers
- · Inefficient AI deployment strategies
Improved model compression and efficiency through better-informed knowledge distillation techniques.
Faster deployment of capable AI models on devices with limited computational resources.
Increased accessibility and democratization of advanced AI capabilities due to lower resource requirements.
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