Image recognition method based on deep course learning

Abstract

The invention discloses an image recognition method based on deep course learning, and belongs to the field of image recognition. The method comprises the following steps: constructing teacher and student networks based on a deep convolutional neural network; performing image classification training on the teacher network by using a training sample, and predicting the probability that the trainingsample belongs to each category; calculating the difference between the prediction of the teacher network and the labels to update the parameters; transmitting the prediction information to a studentnetwork; training the student network; guiding student network training according to the prediction information result of the teacher network; calculating a difference updating parameter between thestudent network prediction result and the label; completing student network classification training; and the trained student network realizes recognition and classification of the images. According tothe method, the process of human learning from easiness to difficulty is simulated, the training process is reasonable, the workload is greatly reduced, the network parameters are updated quickly, the influence of the samples is balanced by gradient differences generated by different samples, the prediction precision is higher, and the performance is more reliable and stable.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to an image recognition method based on deep course learning. Background technique [0002] Due to the large increase in computing power and the increase in training data, the training conditions of deep neural networks have been met. Due to its strong fitting ability, deep neural networks have achieved the best results in many tasks. However, since the parameters of deep neural networks are updated based on a large number of samples, samples play a crucial role in the training process. Therefore, the training work is intensive, the training process is complicated, and the parameters of the deep neural network are updated slowly. [0003] At the same time, the deep convolutional neural network will calculate the gradient of each parameter according to the prediction results of the training samples and the correctly classified labels of the deep convolutional neural networ...

AI Technical Summary

Problems solved by technology

In the existing technology, the deep convolutional neural network is often punished due to the large difference between the prediction result and the correct labeling result, which cannot fit the real labeling result.

Images