Method of improving performance of convolutional neural network based on linear discriminant analysis criterion

Abstract

The invention discloses a method of improving performance of a convolutional neural network based on a linear discriminant analysis criterion, which belongs to the field of deep learning convolutional neural networks, the field of brain cognition and the field of computer vision image classification. The method comprises the following steps: 1) to-be-processed image sets are divided into a training set, a verification set and a test set; 2) a convolutional neural network model is selected; 3) one layer in the convolutional neural network model in the second step is selected, regularization constraints based on the linear discriminant analysis criterion are carried out on features of the selected layer, and a new convolutional neural network model is formed; and 4) according to mini-batch-based stochastic gradient descent method, the training set is used for training the new convolutional neural network model, and after the new convolutional neural network model is well trained, the well-trained convolutional neural network model tests to-be-classified images, and classified prediction is completed. Experimental results show that the method of the invention can significantly improve the convolutional neural network image classification precision.

Description

Technical field: [0001] The invention relates to the field of deep learning convolutional neural network, the field of brain cognition and the field of computer vision image classification, in particular to a method for improving the performance of the convolutional neural network and a method for improving the performance of the convolutional neural network for image classification. Background technique: [0002] The current deep convolutional neural network has been widely used in various fields of computer vision, such as image classification, target detection and localization, and image retrieval. For a long time, the methods to improve the performance of convolutional network image classification can be roughly divided into two types: one is to increase the scale of the network structure, that is, to increase the number of layers of the network and the number of nodes in each layer; the other is to use larger scale training set. [0003] Increasing the scale of the net...

AI Technical Summary

Problems solved by technology

In view of the above two situations, some scholars have recently tried to make some small modifications to the structure of the convolutional neural network, but these small skills are based on experiment-driven, some skills summarized through a large number of experiments, lack of theory Of course, it is difficult to systematically improve network performance

Images