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不同池化模型的卷积神经网络学习性能研究
引用本文:刘万军,梁雪剑,曲海成.不同池化模型的卷积神经网络学习性能研究[J].中国图象图形学报,2016,21(9):1178-1190.
作者姓名:刘万军  梁雪剑  曲海成
作者单位:辽宁工程技术大学软件学院, 葫芦岛 125105,辽宁工程技术大学软件学院, 葫芦岛 125105,辽宁工程技术大学软件学院, 葫芦岛 125105
基金项目:国家自然科学基金项目(61172144);辽宁省教育厅科学技术研究一般项目(L2015216)
摘    要:目的 基于卷积神经网络的深度学习算法在图像处理领域正引起广泛关注。为了进一步提高卷积神经网络特征提取的准确度,加快参数收敛速度,优化网络学习性能,通过对比不同的池化模型对学习性能的影响提出一种动态自适应的改进池化算法。方法 构建卷积神经网络模型,使用不同的池化模型对网络进行训练,并检验在不同迭代次数下的学习结果。在现有算法准确率不高和收敛速度较慢的情况下,通过使用不同的池化模型对网络进行训练,从而构建一种新的动态自适应池化模型,并研究在不同迭代次数下其对识别准确率和收敛速度的影响。结果 通过对比实验发现,使用动态自适应池化算法的卷积神经网络学习性能最优,在手写数字集上的收敛速度最高可以提升18.55%,而模型对图像的误识率最多可以降低20%。结论 动态自适应池化算法不但使卷积神经网络对特征的提取更加精确,而且很大程度地提高了收敛速度和模型准确率,从而达到优化网络学习性能的目的。这种模型可以进一步拓展到其他与卷积神经网络相关的深度学习算法。

关 键 词:深度学习  卷积神经网络  图像识别  特征提取  算法收敛  动态自适应池化
收稿时间:2015/11/24 0:00:00
修稿时间:2016/4/20 0:00:00

Learning performance of convolutional neural networks with different pooling models
Liu Wanjun,Liang Xuejian and Qu Haicheng.Learning performance of convolutional neural networks with different pooling models[J].Journal of Image and Graphics,2016,21(9):1178-1190.
Authors:Liu Wanjun  Liang Xuejian and Qu Haicheng
Institution:College of Software, Liaoning Technical University, Huludao 125105, China,College of Software, Liaoning Technical University, Huludao 125105, China and College of Software, Liaoning Technical University, Huludao 125105, China
Abstract:Objective Deep learning algorithms based on convolutional neural networks are attracting attention in the field of image processing. To improve the accuracy of the feature extraction process and the convergence rate of parameters, as well as optimize the learning performance of the network, an improved dynamic adaptive pooling algorithm is proposed, which compares the effect of different pooling models on learning performance. Method A convolutional neural network model, which is trained with different pooling models, is constructed. The results of the trained model are verified in different iterations. To compensate for low accuracy and slow convergence speed, a dynamic adaptive pooling model is proposed, which trains the network with different pooling models. The effect of the model on the accuracy and convergence rate in different iterations are then studied. Result Contrast experiment shows that the dynamic pooling model has optimal learning performance. The maximum improvement of the convergence rate on handwritten database is 18.55% and the maximum decrement of the accuracy rate is 20%. Conclusion A dynamic adaptive pooling algorithm can improve the accuracy of feature extraction, convergence rate, and accuracy of the convolutional neural network, thereby optimizing network learning performance. The dynamic adaptive pooling model can be further extended to other deep learning algorithms related to convolutional neural networks.
Keywords:deep learning  convolutional neural network  image recognition  feature extraction  algorithm convergence  dynamic adaptive pooling
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