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arXiv · 2502.18510

Multi-Teacher Knowledge Distillation with Reinforcement Learning for Visual Recognition

Chuanguang Yang·Xinqiang Yu·Han Yang·Zhulin An·Chengqing Yu·Libo Huang·Yongjun Xu
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Abstract

Multi-teacher Knowledge Distillation (KD) transfers diverse knowledge from a teacher pool to a student network. The core problem of multi-teacher KD is how to balance distillation strengths among various teachers. Most existing methods often develop weighting strategies from an individual perspective of teacher performance or teacher-student gaps, lacking comprehensive information for guidance. This paper proposes Multi-Teacher Knowledge Distillation with Reinforcement Learning (MTKD-RL) to optimize multi-teacher weights. In this framework, we construct both teacher performance and teacher-student gaps as state information to an agent. The agent outputs the teacher weight and can be updated by the return reward from the student. MTKD-RL reinforces the interaction between the student and teacher using an agent in an RL-based decision mechanism, achieving better matching capability with more meaningful weights. Experimental results on visual recognition tasks, including image classification, object detection, and semantic segmentation tasks, demonstrate that MTKD-RL achieves state-of-the-art performance compared to the existing multi-teacher KD works.

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Chuanguang Yang, Xinqiang Yu, Han Yang, Zhulin An, Chengqing Yu, Libo Huang, Yongjun Xu. 2025-02-22. Multi-Teacher Knowledge Distillation with Reinforcement Learning for Visual Recognition. https://arxiv.org/abs/2502.18510

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