Distilling deep neural networks with reinforcement learning

Deep architecture can improve performance of neural networks whereas it increases the computational complexity. Compressing networks is the key to solve this problem. The framework Knowledge Distilling (KD) compresses cumbersome networks well. It improved mimic learning, enabling knowledge to be transferred from cumbersome networks to compressed networks without constraint of architectures. Inspired by AlphaGo Zero, this paper proposed an algorithm combining KD with reinforcement learning to compress networks on changing datasets. In this algorithm, the compressed networks interact with the environment made by KD to produce datasets that are appropriate w.r.t the model. Monte Carlo Tree Search (MCTS) of AlphaGo Zero is used to produce the datasets by making a trade-off between the prediction of compressed networks and the knowledge. In experiments, the algorithm proved to be effective in compressing networks by training ResNet on CIFAR datasets, with mean squared error as the object function.