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Using of a Convolutional Neural Network with Changing Receptive Fields in the Tasks of Image Recognition

机译:利用卷积神经网络与改变图像识别任务中的接受领域

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In the article synthesis procedure of mathematical model features of convolution neural network (CNN) is described. In order to improve the generalization capability of the network the training set is generated by adding a distorted image with changing of CNN receptive fields. This fact differs given procedure from the known procedures. We propose the reduction algorithm of an extended training set and the synthesis algorithm of features for CNN with non-standard receptive fields. The experiments results of the developed algorithms were shown in the article in order to assess of generalization capability changes of the convolution neural network. The experiments were performed with the hardware platform of the "Mechatronics" stand (SPA "Android techniques", Russia).
机译:在卷积神经网络(CNN)的数学模型特征的文章综合过程中,描述于神经网络(CNN)。为了改善网络的泛化能力,通过增加CNN接收字段的变形图像来生成训练集。这一事实与已知程序的给定程序不同。我们提出了具有非标准接收领域的CNN的扩展训练集的减少算法和CNN的综合算法。在物品中示出了发育算法的实验结果,以便评估卷积神经网络的泛化能力变化。使用“机电一体化”支架的硬件平台(SPA“Android Techniques”,俄罗斯进行实验。

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