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

• URL: http://arxiv.org/abs/2505.06270v1
• Date: Tue, 06 May 2025 04:04:30 GMT
• Title: Importance Analysis for Dynamic Control of Balancing Parameter in a Simple Knowledge Distillation Setting
• Authors: Seongmin Kim, Kwanho Kim, Minseung Kim, Kanghyun Jo,
• Abstract summary: knowledge distillation (KD) stands out for its strong empirical performance.<n> KD is most effective when the influence of the distillation loss outweighs that of the downstream-task loss.<n>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.
• Score: 2.5536554335016417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

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