Knowledge distillation data enhancement method and system based on generative adversarial network

A knowledge and data technology, applied in biological neural network models, neural learning methods, character and pattern recognition, etc., can solve problems such as errors, data enhancement, multi-non-information noise, and labeling area offset.

Active Publication Date: 2021-05-18
WUHAN INSTITUTE OF TECHNOLOGY
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Problems solved by technology

[0003] The discarding, filling and fusion strategies of existing data enhancement often introduce more non-informative noise, reduce the signal-to-noise ratio of samples, and misidentify target regions during semantic segmentation.
In addition, the difference betw

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  • Knowledge distillation data enhancement method and system based on generative adversarial network
  • Knowledge distillation data enhancement method and system based on generative adversarial network
  • Knowledge distillation data enhancement method and system based on generative adversarial network

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Embodiment Construction

[0048] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0049] A knowledge distillation data enhancement system based on generative confrontation network according to an embodiment of the present invention, including an input module, an enhanced image module, a network training module and a knowledge distillation module;

[0050] The input module is used to read in the data set to be tested;

[0051]The enhanced image module is used to discard the region to obtain a patch image image, and use the generation confrontation network to generate the enhanced image; the region discarding is to use the mask to multiply the image; the generation confrontation network is to use the generation confrontation network according to Patch images to generate enhanced images with the same data distribution;

[0052] The network training module is used to obtain the neural network classifier according to t...

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Abstract

According to the knowledge distillation data enhancement method and system based on the generative adversarial network provided by the invention, through an easy-to-operate data enhancement mode, the positioning capability and the recognition accuracy of the convolutional network to the industrial data set are improved. According to the method, a generative adversarial network is combined to efficiently fit sample data distribution and an algorithm for improving the fault-tolerant rate of a neural network model in knowledge distillation, non-information noise is not introduced when an enhanced sample is generated, and the robustness of the model to an error tag is improved. According to the method, misleading on the model caused by marking errors of positive and negative samples of the industrial data set is reduced, the representation expression performance of the industrial data set on a semantic segmentation task is improved, and the sample feature learning ability of the model under the condition that feature granularity distinguishing is small is improved; The invention plays an important role in applications such as automobile part detection and manufacturing, railway part positioning and the like.

Description

technical field [0001] The invention belongs to the technical field of data enhancement, and in particular relates to a knowledge distillation data enhancement method and system based on generative confrontation networks. Background technique [0002] Data augmentation is a method of increasing the amount of data through a certain transformation method on the original data set. Its purpose is to have a sufficient proportion of samples when training a neural network classifier so that the classifier can adjust millions of parameters to the best point of model loss during the training process. [0003] The discarding, filling and fusion strategies of existing data enhancement often introduce more non-informative noise, reduce the signal-to-noise ratio of samples, and misidentify target regions during semantic segmentation. In addition, the difference between positive and negative samples of industrial data is small, and there are errors in the manual labeling process, and the...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/24G06F18/214
Inventor 徐子昕鲁统伟
Owner WUHAN INSTITUTE OF TECHNOLOGY
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