Lasso Regression Based Channel Pruning for Efficient Object Detection Model Deep convolutional neural networks have achieved remarkable performance on object detection tasks. Regression based models include YOLO and SSD are faster and more accurate, but they still run slowly on devices with limited computational and memory resources. Many work focus on network pruning so that they can be effectively deployed. However, most of them require expert knowledge and massive experiments to determine which layers to prune, and how many channels to prune in every single layer. In this paper, we firstly define the importance score of a channel as the product of its L1norm of weights and scaling factor of batch normalization layer. Then we adopt Lasso regression and finegrained regularization coefficient to learn sparse scaling factor. By pruning channels with small importance score then retraining, we can get a compact model without significant accuracy drop. Compared to other rulebased pruning method, our method achieves higher accuracy on Pascal VOC2007 dataset while requires less human effort on channel selection.

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).