卷积神经网络的正则化方法综述OA北大核心CSTPCD

In recent years,convolutional neural networks have been widely used in various fields of computer vision and achieved remarkable results.Regularization method is an important part of convolutional neural network,which helps to avoid the overfitting phenomenon of convolutional neural network in the process of model training.There are fewer reviews on regularization methods for convolutional neural networks,and most of them lack a summary of the newly proposed regularization methods.Firstly,this paper conducted a detailed summary of the literature on regularization methods in convolutional neural networks,and classified the regularization methods into parameter regularization,data regularization,label regularization and combinatorial regularization.After that,on the public datasets such as ImageNet,it compared and analyzed the advantages and disadvantages of different regularization methods based on evaluation indexes such as top-1 accuracy and top-5 accuracy.Finally,it discussed the future research trends and work directions of regularization methods for convolutional neural network.