Importance-Aware Filter Selection for Convolutional Neural Network Acceleration

Convolutional Neural Networks(CNNs) are widely used in many fields, including artificial intelligence, computer vision and video coding. However, CNNs are typically over-parameterized and contain significant redundancy. Traditional model acceleration methods mainly rely on specific manual rules. This usually leads to sub-optimal results with relatively limited compression ratio. Recent works have deployed the self-learning agent on the layer-level acceleration but still combined with human-designed criterias. In this paper, we proposed a filter-based model acceleration method to directly and automatically decide which filters should be pruned with the reinforcement learning method DDPG. We designed a novel reward function with the reward shaping technique for the training process. Our method is utilized on the models trained on MNIST and CIFAR-10 datasets and achieves both higher acceleration ratio and less accuracy loss than the conventional methods simultaneously.