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IMAGE CLASSIFICATION METHOD FOR EQUIVARIANT CONVOLUTIONAL NETWORK MODEL BASED ON PARTIAL DIFFERENTIAL OPERATOR
IMAGE CLASSIFICATION METHOD FOR EQUIVARIANT CONVOLUTIONAL NETWORK MODEL BASED ON PARTIAL DIFFERENTIAL OPERATOR
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机译：基于局部差分运算符的等级卷积网络模型图像分类方法
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摘要
An image classification method for an equivariant convolutional network model based on a partial differential operator. For an input layer and an intermediate layer of a convolutional network model, an equivariant convolution of the input layer and an equivariant convolution of the intermediate layer are respectively designed on the basis of a partial differential operator, and an equivariant convolutional network model PDO-eConv is constructed and performed model training; an input of the model PDO-eConv is image data, and an output of the model PDO-eConv is the predictive classification of an image, so that efficient image classification and recognition visual analysis is achieved. The method can provide a better parameter sharing mechanism, and achieve a lower image classification error rate.
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