ImportanceAware 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 overparameterized and contain significant redundancy. Traditional model acceleration methods mainly rely on specific manual rules. This usually leads to suboptimal results with relatively limited compression ratio. Recent works have deployed the selflearning agent on the layerlevel acceleration but still combined with humandesigned criterias. In this paper, we proposed a filterbased 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 CIFAR10 datasets and achieves both higher acceleration ratio and less accuracy loss than the conventional methods simultaneously.

A Onestep Pruningrecovery Framework for Acceleration of Convolutional Neural Networks Acceleration of convolutional neural network has received increasing attention during the past several years. Among various acceleration techniques, filter pruning has its inherent merit by effectively reducing the number of convolution filters. However, most filter pruning methods resort to tedious and timeconsuming layerbylayer pruningrecovery strategy to avoid a significant drop of accuracy. In this paper, we present an efficient filter pruning framework to solve this problem. Our method accelerates the network in onestep pruningrecovery manner with a novel optimization objective function, which achieves higher accuracy with much less cost compared with existing pruning methods. Furthermore, our method allows network compression with global filter pruning. Given a global pruning rate, it can adaptively determine the pruning rate for each single convolutional layer, while these rates are often set as hyperparameters in previous approaches. Evaluated on VGG 16 and ResNet50 using ImageNet, our approach outperforms several stateoftheart methods with less accuracy drop under the same and even much fewer floatingpoint operations (FLOPs).