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Machine learning model training method and device and image classification method and device

A technology of a machine learning model and a training method is applied in the fields of training methods, electronic equipment and non-volatile computer-readable storage media, image classification devices, and training devices for machine learning models, and can solve the problems of poor training effect and training data. There are deviations and high training data costs, so as to increase the amount of training data, reduce costs, and improve training effects.

Pending Publication Date: 2021-12-17
BEIJING WODONG TIANJUN INFORMATION TECH CO LTD +1
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AI Technical Summary

Problems solved by technology

[0004] The inventors of the present disclosure found the following problems in the above-mentioned related technologies: the cost of obtaining a large amount of labeled training data is high, and the generated training data deviates from the real situation, resulting in poor training effect

Method used

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  • Machine learning model training method and device and image classification method and device
  • Machine learning model training method and device and image classification method and device
  • Machine learning model training method and device and image classification method and device

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Embodiment Construction

[0036] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that relative arrangements of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

[0037] At the same time, it should be understood that, for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0038] The following description of at least one exemplary embodiment is merely illustrative in nature and in no way intended as any limitation of the disclosure, its application or uses.

[0039] Techniques, methods and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such techniques, methods and devices should be considered part of t...

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Abstract

The invention relates to a machine learning model training method and device and an image classification method and device, and relates to the technical field of artificial intelligence. The training method comprises the steps that a first machine learning model is utilized to extract a feature vector of a to-be-processed picture, a first classification result of the to-be-processed picture is determined according to the feature vector, and the to-be-processed picture belongs to a first data field or a second data field; according to the feature vector, a second machine learning model is utilized to determine a second classification result of the to-be-processed picture, and the second classification result comprises a classification result of the to-be-processed picture in the first data domain and a classification result of the to-be-processed picture in the second data domain; and according to the first classification result and the second classification result, adversarial training is performed on the first machine learning model and the second machine learning model, so that the accuracy of the second classification result is lower than a threshold, and the trained first machine learning model is used for picture classification.

Description

technical field [0001] The present disclosure relates to the technical field of artificial intelligence, and in particular to a machine learning model training method, a machine learning model training device, an image classification method, an image classification device, electronic equipment, and a non-volatile computer-readable storage medium. Background technique [0002] As an image classification method, image semantic segmentation is an important topic in the field of computer vision. Its purpose is to divide each pixel of the input image into a unique semantic label. Image semantic segmentation has important applications in many fields, such as autonomous driving, image generation, etc. Therefore, it is particularly important to train machine learning models for image semantic segmentation. [0003] In related technologies, it usually relies on a large amount of labeled training data for training; or uses computer vision technology to generate training data through ...

Claims

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Application Information

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IPC IPC(8): G06N20/00G06K9/62
CPCG06N20/00G06F18/24
Inventor 申童张炜梅涛
Owner BEIJING WODONG TIANJUN INFORMATION TECH CO LTD
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