Multi-Teacher Knowledge Distillation with Reinforcement Learning for Visual Recognition

TL;DR

This paper introduces a reinforcement learning-based method for multi-teacher knowledge distillation that dynamically optimizes teacher weights, leading to improved performance across various visual recognition tasks.

Contribution

It proposes a novel RL framework that considers teacher performance and gaps to adaptively assign weights, enhancing multi-teacher knowledge distillation effectiveness.

Findings

Achieves state-of-the-art results on image classification, object detection, and segmentation.

Demonstrates superior weight optimization over existing methods.

Improves student network performance through adaptive teacher weighting.

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.