卷积神经网络的正则化方法综述

近年来，卷积神经网络已经广泛应用于计算机视觉各个领域中并取得了显著的效果。正则化方法是卷积神经网络的重要组成部分，它能避免卷积神经网络在模型训练的过程中出现过拟合现象。目前关于卷积神经网络正则化方法的综述较少，且大多缺乏对新提出的正则化方法的总结。首先对卷积神经网络中的正则化方法相关文献进行详细的总结和梳理，将正则化方法分为参数正则化、数据正则化、标签正则化和组合正则化；然后在ImageNet等公开数据集上，基于top-1 accuracy、top-5 accuracy等评价指标，对不同正则化方法的优缺点进行对比分析；最后讨论了卷积神经网络的正则化方法未来的研究趋势和工作方向。

Survey on regularization methods for convolutional neural networks

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 networks.