Development method and system of small sample classification model based on graph convolutional neural network

A technology of convolutional neural network and classification model, which is applied in the direction of biological neural network model, neural architecture, character and pattern recognition, etc., and can solve problems such as poor performance

Active Publication Date: 2021-01-05
INST OF AUTOMATION CHINESE ACAD OF SCI
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[0005] In order to solve the above-mentioned problems in the prior art, that is, the performance of existing models is poor when applied to new tasks and a large number of labeled t

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  • Development method and system of small sample classification model based on graph convolutional neural network
  • Development method and system of small sample classification model based on graph convolutional neural network
  • Development method and system of small sample classification model based on graph convolutional neural network

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[0064] The application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the relevant inventions, rather than to limit the inventions. It should also be noted that, for the convenience of description, only the parts related to the related invention are shown in the drawings.

[0065] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and embodiments.

[0066] The invention provides a method for developing a small-sample classification model based on a graph convolutional neural network. This method can well solve the problems of low efficiency, low accuracy and high complexity in the collection of small-sample classif...

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Abstract

The invention belongs to the field of computer vision, particularly relates to a development method and system of a small sample classification model based on a graph convolutional neural network, andaims to solve the problems that an existing model is poor in performance and needs a large number of training samples with labels when applied to a new task. The method comprises the steps of extracting a knowledge graph formed by an undirected graph, and obtaining knowledge information related to a task; extracting experience information of the original model in the training process of the old task; fusing the knowledge information and the empirical information to form a new fusion graph; and obtaining a new task-oriented cognitive developed classification model by establishing a relationship between a new task category and an old task category and carrying out model training. According to the method, in the task migration process, a new task can achieve rapid and accurate visual migration without providing a large number of samples with labels, the utilization rate of the model is greatly increased, the cost of training the model is reduced, and the time of training the model is shortened.

Description

technical field [0001] The invention belongs to the field of computer vision, and in particular relates to a method and system for developing a small-sample classification model based on a graph convolutional neural network. Background technique [0002] With the continuous development of related technologies in the field of computer vision, the application range of computer vision technology is becoming more and more extensive. While the visual algorithm is gradually maturing, the model's dependence on large-scale image samples has become an unavoidable problem in model training. For fields where sample collection is difficult, even in fields that do not have large-scale samples, the collection of labeled samples has become a difficult problem that restricts the development of computer vision technology. Due to the continuous expansion of the field of human cognition, new task categories are gradually discovered. However, the test task objects of most target recognition m...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/241G06F18/25G06F18/214
Inventor 杨旭张昕悦刘智勇张璐
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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