Importance Analysis for Dynamic Control of Balancing Parameter in a Simple Knowledge Distillation Setting

TL;DR

This paper investigates how to dynamically adjust the balancing parameter in knowledge distillation to improve model compression, providing a mathematical rationale for its importance in optimizing training.

Contribution

It introduces a mathematical analysis demonstrating the necessity of dynamic adjustment of the balancing parameter during knowledge distillation.

Findings

Dynamic adjustment of the balancing parameter improves distillation effectiveness.

Mathematical rationale supports the importance of parameter tuning during training.

The approach enhances the efficiency of knowledge transfer in model compression.

Abstract

Although deep learning models owe their remarkable success to deep and complex architectures, this very complexity typically comes at the expense of real-time performance. To address this issue, a variety of model compression techniques have been proposed, among which knowledge distillation (KD) stands out for its strong empirical performance. The KD contains two concurrent processes: (i) matching the outputs of a large, pre-trained teacher network and a lightweight student network, and (ii) training the student to solve its designated downstream task. The associated loss functions are termed the distillation loss and the downsteam-task loss, respectively. Numerous prior studies report that KD is most effective when the influence of the distillation loss outweighs that of the downstream-task loss. The influence(or importance) is typically regulated by a balancing parameter. This paper provides a mathematical rationale showing that in a simple KD setting when the loss is decreasing, the balancing parameter should be dynamically adjusted