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

arXiv: https://arxiv.org/abs/2505.06270 · HTML: https://mlsanity.com/paper/2505.06270/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General knowledge distillation methods, one of 12 directions in Knowledge distillation.
- Builds on 13 of its 13 references that are in the map.
- First posted on arXiv 6 May 2025.

## Placed in

- Knowledge distillation / [General knowledge distillation methods](https://mlsanity.com/distill/general-knowledge-distillation-methods/)

## Builds on (most cited first, 13 of 13)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Gradient Surgery for Multi-Task Learning](https://mlsanity.com/paper/2001.06782/) (2020)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [Towards Understanding Knowledge Distillation](https://mlsanity.com/paper/2105.13093/) (2021)
- [Stiffness: A New Perspective on Generalization in Neural Networks](https://mlsanity.com/paper/1901.09491/) (2019)
- [Even your Teacher Needs Guidance: Ground-Truth Targets Dampen Regularization Imposed by Self-Distillation](https://mlsanity.com/paper/2102.13088/) (2021)
- [Knowledge Distillation Performs Partial Variance Reduction](https://mlsanity.com/paper/2305.17581/) (2023)
- [Can Students Beyond The Teacher? Distilling Knowledge from Teacher's Bias](https://mlsanity.com/paper/2412.09874/) (2024)

## Abstract (arXiv)

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