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Image recognition method of residual neural network based on implicit Euler jump connection

A skip connection and image recognition technology, applied in biological neural network models, character and pattern recognition, neural architecture, etc., to achieve the effects of stable prediction accuracy, strong robustness and credibility, and improved accuracy and effectiveness

Active Publication Date: 2020-07-10
PEKING UNIV
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Problems solved by technology

However, no previous work has proposed a practical and feasible improved structure to improve the robustness of traditional ResNet from the perspective of the stability of numerical ODE, and proposed a more robust image recognition method based on this structure

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  • Image recognition method of residual neural network based on implicit Euler jump connection
  • Image recognition method of residual neural network based on implicit Euler jump connection
  • Image recognition method of residual neural network based on implicit Euler jump connection

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Embodiment Construction

[0041] Below in conjunction with accompanying drawing, further describe the present invention through embodiment, but do not limit the scope of the present invention in any way.

[0042] The present invention can be applied to any application related to image recognition, such as face recognition, object detection, text recognition, etc. The following embodiments apply the method of the present invention to image classification problems and test the robustness of the method. The specific implementation mainly includes four steps, which are data collection, data preprocessing, building and training the model for feature extraction and feature recognition, and testing the classification performance and robustness of the model. Among them, the residual network model includes both feature extraction and feature recognition processes, and its performance is superior to other traditional methods. However, applying the residual network model containing implicit Euler skip connection ...

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Abstract

The invention discloses an image recognition method of a residual neural network based on implicit Euler jump connection. An implicit Euler numerical method is combined with jump connection in a residual error network model to establish an improved model with higher robustness, namely a residual error neural network containing implicit Euler jump connection, the input of the improved model is image data and corresponding labels, and the output of the improved model is prediction classification of images, so that more stable image recognition is realized. The image recognition method based on the residual neural network containing the implicit Euler jump connection provided by the invention has stronger robustness and credibility, can improve the accuracy and effectiveness of image recognition, and can be applied to various image recognition scenes such as face recognition, character recognition and the like.

Description

technical field [0001] The present invention relates to the field of deep neural network structure design technology and image recognition technology, in particular to a method for image recognition based on a residual neural network model containing Implicit Euler Skips (IE-Skips, namely Implicit Euler Skip Connections), It can be applied to various image recognition scenarios such as face recognition and text recognition. Background technique [0002] With the rapid development of image processor (GPU) computing power in recent years and the increasing amount of data that people can obtain, deep neural networks have been widely used in computer vision, image processing, and natural language processing. Since the breakthrough of the deep neural network on the ImageNet classification task in 2012, researchers have proposed a variety of different networks, and their structures are not limited to the classic feedforward neural network structure. In a feedforward network struc...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V40/172G06V20/56G06N3/045G06F18/214
Inventor 林宙辰李明杰何翎申
Owner PEKING UNIV
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