Multiobjective ResNet pruning by means of EMOAs for remote sensing scene classification

Abstract

Convolutional neural networks have achieved remarkable success in the field of computer vision. However, due to their high storage and expensive computations, recently, there has been a lot of work focusing on reducing the complexity of convolutional neural networks. In this work, we propose a random filter pruning method by means of evolutionary multiobjective optimization algorithms to accelerate the Siamese ResNet-50 for remote sensing scene classification. We have conduct experiments on NWPU-RESISC45, UC Merced Land-Use and SIRI-WHU datasets for performance evaluation of the proposed method. The experimental results demonstrate that the classification performance of our pruned model has been improved while keeping a certain degree of sparsity of the model.