Construction method of multi-task classification network based on orthogonal loss function

Abstract

The invention provides a construction method of a multi-task classification network based on an orthogonal loss function. The constructed multi-task classification network simulates a human learning process, a deep convolutional neural network is used as a hidden layer to simulate the brain of a human to perform deep feature extraction, a tree classifier is used as a task-related output layer to perform progressive classification, and the recognition process forms different learning tasks Characteristics obtained by different tasks to better meet respective requirements. When the classifier completes a coarse classification task, the depth features of the same coarse class are more aggregated; and when the fine classification task is completed, the depth features of different fine classesare more discrete, and the task output layer features of different classification tasks are distinguished, so that classifiers of different hierarchies can obtain features better matched with different classification tasks, useless features are removed, and the classification accuracy is improved.

Description

technical field [0001] The invention relates to the field of image classification, in particular to a multi-task classification construction method. Background technique [0002] In recent years, photo recognition has become more and more widely used in field exploration and daily life. This is thanks to the development of deep learning. The current best-performing tool for feature extraction is the deep convolutional neural network. As we all know, the deep convolutional neural network can not only extract edge information in the shallow layer, but also the semantic information of the feature will become more and more abstract as the number of layers deepens, and the obtained features are closer to human cognitive behavior. [0003] Subsequently, multi-task classification networks have gradually entered people's sight. The different tasks of the multi-task classification network assist each other, and the different tasks of the same network are trained at the same time, ...

AI Technical Summary

Problems solved by technology

However, due to the sharing of hidden layer parameters, the extracted features are intersected. Therefore, the multi-task classification network lacks the distinction between the extracted common features and unique features in the process of performing different classification tasks.

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