Reinforced Knowledge Distillation for Time Series Regression

Qing Xu, Keyu Wu, Min Wu, Kezhi Mao, Xiaoli Li, Zhenghua Chen

Abstract: As one of the most popular and effective methods in model compression, knowledge distillation (KD) attempts to transfer knowledge from single or multiple large-scale networks (i.e., Teachers ) to a compact network (i.e., Student ). For the multiteacher scenario, existing methods either assign equal or fixed weights for different teacher models during distillation, which can be inefficient as teachers might perform variously or even oppositely on different training samples. To address this issue, we propose a novel reinforced knowledge distillation method with negatively correlated teachers which are generated via negative correlation learning. The negatively correlated teachers would encourage teachers to learn different aspects of data and thus the ensemble of them can be more comprehensive and suitable for multiteacher KD. Subsequently, a reinforced KD algorithm is proposed to dynamically employ proper teachers for different training instances via dueling double deep Q-network (DDQN). Our proposed method complements the existing KD procedure on teacher generation and selection. Extensive experimental results on two real-world time series regression tasks clearly demonstrate that the proposed approach could achieve superior performance over state-of-the-art (SOTA) methods. The PyTorch implementation of our proposed approach is available at https://github.com/xuqing88/RL-KD-for-time-series-regression .