Multi-classifier-based convolutional neural network quick classification method

Abstract

The invention discloses a multi-classifier-based convolutional neural network quick classification method. According to the method, an activation function and a linear classifier are added after each convolutional layer except the last one. During network training, image features of the convolutional layers are obtained firstly and the classifiers after the convolutional layers are trained by using cross entropy loss functions. After the training, the activation functions are adjusted to enable the classification accuracy to reach the best. During execution of an image classification task, the classifiers of all the layers are activated in sequence in a forward propagation process, the image features after convolution are subjected to calculation analysis through the classifiers, a judgment value is obtained, and if the judgment value meets the activation requirements of the activation functions, classification results of the classifiers are directly output and the classification process is ended. On the contrary, forward propagation activates the next convolutional layer to continue executing the classification task. The method can classify images easy to classify in advance to finish the forward propagation process of a network, so that the network classification speed is increased and the classification time is shortened; and the method has good practical value.

Description

technical field [0001] The invention belongs to the field of image classification of convolutional neural network in deep learning. By improving the structure of the convolutional neural network, the classification speed of the network is improved, and the time of image classification is saved. Background technique [0002] Convolutional neural network (CNN) is a representative deep learning method, which is widely and efficiently applied to the research of computer vision problems. This is mainly due to its excellent learning ability for high-dimensional data features. In recent years, with the emergence of related learning technology, optimization technology and hardware technology, convolutional neural network has achieved explosive development. The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is a standard challenge for large-scale object recognition for workers. In recent years, convolutional neural networks have been widely used in classification compe...

AI Technical Summary

Problems solved by technology

In addition, smartphones and portable devices usually do not have powerful computing capabilities, and applications such as scene recognition on these devices also require fast response

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