Identifying and Pruning Redundant Structures for Deep Neural Networks Deep convolutional neural networks have achieved considerable success in the field of computer vision. However, it is difficult to deploy stateoftheart models on resourceconstrained platforms due to their high storage, memory bandwidth, and computational costs. In this paper, we propose a structured pruning method which employs a threestep process to reduce the resource consumption of neural networks. First, we train an initial network on the training set and evaluate it on the validation set. Next, we introduce an iterative pruning and finetuning algorithm to identify and prune redundant structures, which results in a pruned network with a compact architecture. Finally, we train the pruned network from scratch on both the training set and validation set to obtain the final accuracy on the test set. In the experiments, our pruning method significantly reduces the model size (by 87.2% on CIFAR10), saves inference time (53.3% on CIFAR10), and achieves better performance as compared to recent stateoftheart methods.

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).