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Deep learning sample annotation method based on online education big data

A deep learning and sample labeling technology, applied in multimedia data retrieval, electronic digital data processing, metadata multimedia retrieval, etc., can solve the problems of inaccurate deep learning output, inaccurate samples, and inability to use pictures, so as to improve user experience. , release boring, avoid the effect of subjectivity

Active Publication Date: 2018-06-15
BEIJING DASHENG ON LINE TECH CO LTD
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  • Abstract
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  • Claims
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AI Technical Summary

Problems solved by technology

[0002] The marking methods for online education files (video, audio, pictures, etc.) mainly include: first, it is a pure manual labor to realize the marking by screening the files by a single person, and the subjectivity of a single person is too strong, resulting in the marking Inaccurate; the second type, based on automated annotation, is still in the research stage, and cannot be applied to image annotation, let alone video and audio
[0003] The disadvantage of the existing technology is that if thousands of files are marked, it is necessary to repeatedly watch audio, video or pictures for screening, which consumes a lot of manpower and material resources, but is very subjective
As a result, the labeled samples are not accurate enough, and finally the output of deep learning is inaccurate

Method used

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  • Deep learning sample annotation method based on online education big data
  • Deep learning sample annotation method based on online education big data

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Embodiment Construction

[0051] The present invention is described in detail below with reference to accompanying drawing and embodiment:

[0052] attached Figure 1-2 It can be seen that a deep learning sample labeling method based on online education big data,

[0053] Enter M pieces of online education data that need to be marked, and ensure that the input data are of the same category (such as all audio or all video), M is a large amount of data, generally more than 100,000, so as to ensure deep learning training The best models come out.

[0054] Store the online education data to be marked in the database;

[0055] Display N data that needs to be marked through the online education data labeling system; N data comes from unlabeled random data in the database;

[0056] Label the displayed N data;

[0057] When M is greater than N*impressions, randomly select N data from the remaining M-N*impressions, and when it is less than N, select all the remaining data;

[0058] When M is less than N*im...

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Abstract

The present invention relates to a deep learning sample annotation method based on online education big data. The method comprises: inputting M to-be-annotated online education data, displaying the Ndata, and annotating the displayed N data; when M is greater than N* displaying times, selecting N data randomly from the remaining M-N* displaying times, and when M is less than N* displaying times,re-selecting N data randomly from the M data, and setting the displaying number as 1; after the sum of all annotated data is greater than the J multiple of M, taking the number of times of the annotated data greater than K as the valid data conforming to the classification; and obtaining a classification library of different types of data. According to the deep learning sample annotation method based on online education big data disclosed by the present invention, the identification of online education data is improved, the user satisfaction is improved, the user experience is improved, subjectivity of the individual is avoided, the dullness of a single repetitive labor is released, and all annotated users are greatly facilitated.

Description

technical field [0001] The invention relates to an Internet online education system, in particular to a deep learning sample labeling method based on online education big data. Background technique [0002] The marking methods for online education files (video, audio, pictures, etc.) mainly include: first, it is a pure manual labor to realize the marking by screening the files by a single person, and the subjectivity of a single person is too strong, resulting in the marking Inaccurate; the second type, based on automated annotation, is still in the research stage, and cannot be applied to image annotation, let alone video and audio. [0003] The disadvantage of the existing technology is that if thousands of files are marked, it is necessary to repeatedly watch audio, video or pictures for screening, which consumes a lot of manpower and material resources, but is very subjective. As a result, the labeled samples are not accurate enough, and finally the output of deep learn...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06K9/62
CPCG06F16/48G06F18/24
Inventor 熊利陈靖李晓清
Owner BEIJING DASHENG ON LINE TECH CO LTD
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