Channel Pruning via Lookahead Search Guided Reinforcement Learning
Zi Wang, Chengcheng Li
Abstract
Channel pruning has become an effective yet still challenging approach to achieve compact neural networks. It aims to prune the optimal set of filters whose removal results in minimal performance degradation of the slimmed network. Due to the prohibitively vast search space of filter combinations, existing approaches usually use various criteria to estimate the filter importance while sacrificing some precision. Here we present a new approach to optimizing the filter selection in channel pruning with lookahead search guided reinforcement learning (RL). A neural network that takes as input filterrelated features is trained with RL to prune the optimal sequence of filters and maximize the performance of the remaining network. In addition, we employ Monte Carlo tree search (MCTS) to provide a lookahead search for filter selection, which increases the sample efficiency for the RL training. Experiments on MNIST, CIFAR-10, and ILSVRC-2012 validate the effectiveness of our approach compared to both traditional and automated existing channel pruning approaches.