An Image Classification Method Based on Equivariant Convolutional Network Model Based on Partial Differential Operator

Abstract

The invention discloses an image classification method of an equivariant convolution network model based on a partial differential operator. For the input layer and the middle layer of the convolution network model, the equivariant convolution and The equivariant convolution of the middle layer constructs the equivariant convolution network model PDO-eConv and performs model training; the input of the model PDO-eConv is image data, and the output is the prediction and classification of images, so as to realize efficient image classification, recognition and visual analysis . The present invention can provide a better parameter sharing mechanism and achieve a lower error rate of image classification.

Description

technical field [0001] The invention belongs to the technical fields of pattern recognition, machine learning and artificial intelligence, and relates to an image classification method, in particular to an image classification method based on an equivariant convolutional network model of a partial differential operator. Background technique [0002] Over the past few years, convolutional neural network (CNN) models have become the dominant machine learning method for image recognition tasks. Compared with the fully connected network, a significant advantage of using CNN to process images is that they are translationally equivariant: the feature map obtained by first translating the image and then passing through several convolutional layers is the same as first passing the original image through the convolutional layer and then Translating gives the same result. In other words, each layer maintains translational symmetry, i.e. equivariance. Likewise, equivariance brings ab...

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Problems solved by technology

However, in general, the expression ability of the model that can learn partial differential equations is far inferior to that of the CNN network, so the image recognition effect is not ideal.

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