Training sample generation method for industrial big data processing

Abstract

The invention belongs to the field of image processing, and particularly discloses a training sample generation method for industrial big data processing, which comprises the following steps of: constructing various types of industrial image data sets, and dividing a big sample data set and a small sample data set according to data volumes in the various types of industrial image data sets; Constructing an industrial image generative adversarial network and an optimization objective function, and performing iterative training on the industrial image generative adversarial network based on theoptimization objective function to obtain a small sample generation parameter model; And inputting the large sample images in the large sample data set into the small sample generation parameter modelobtained through training to generate small sample images so as to complete generation of training samples. Complex digital image processing operation does not need to be carried out on the industrial image, various kinds of transformation does not need to be carried out on the original industrial image, excessive manual intervention can be avoided, and industrial image generation errors caused by professional quality of operators are reduced.

Description

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AI Technical Summary

Benefits of technology

This technology solves problems when creating high-quality visual representations from captured or recorded imagery without complicated steps like converting them into binary format. It also allows users to adjust parameters such as brightness levels based upon their preferences rather than just selecting one specific type of pixel value. Overall, this process improves efficiency and productivity while reducing human error associated therewith.

Problems solved by technology

Technological Problems Invention describes how current techniques used by industry-related companies generate high volume datasets from different sources (such as cameras or scanners). These dataset often contain inconsistently distributed values due to imperfections during production process. Existing solutions either require manual adjustment or rely on complex mathematical algorithms which may be difficult to interpret accurately.

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