Sample recognition model generation method and device, computer equipment and storage medium
A technology for identifying models and models, applied in the computer field, can solve problems such as low accuracy of sample recognition and model overfitting
Pending Publication Date: 2020-07-24
TENCENT TECH (SHENZHEN) CO LTD
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[0003] However, the current training method of the sample recognition model is generally to train the model by carrying the historical samples with labels, and identify the new samples through the trained model to obtain the category of the samples; however, if carrying There are fewer historical samples of the label, which will lead to overfitting of the trained model, resulting in a lower accuracy of sample recognition of the model
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[0065] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.
[0066] The generation method of the sample identification model provided by this application can be applied to such as figure 1 shown in the application environment. refer to figure 1 , the application environment diagram includes a server 110 . The server 110 refers to a server with a model training function, which can be implemented specifically by an independent server or a server cluster composed of multiple servers. figure 1 In the illustration, the server 110 is an independent server as an example. The server 110 obtains at least two source domain training sample ...
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The invention relates to a sample recognition model generation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring at least two source domain training sample sets and a target domain training sample set; according to the sample set, pre-training each meta-classification network model to be trained one by one to obtain each pre-trained meta-classification network model; training each pre-trained meta-classification network model again according to the target domain training sample set to obtain each trained target domain classificationnetwork model; determining a classification weight corresponding to each trained target domain classification network model according to the source domain training sample set and the target domain training sample set; and generating a trained sample recognition model according to each trained target domain classification network model and the classification weight corresponding to each trained target domain classification network model. By adopting the method, the sample recognition accuracy of the model can be improved.
Description
technical field [0001] The present application relates to the field of computer technology, in particular to a method, device, computer equipment and storage medium for generating a sample recognition model. Background technique [0002] With the development of computer technology, various samples emerge in endlessly; in order to classify the samples, it is necessary to train the corresponding sample recognition model to identify the samples through the sample recognition model. [0003] However, the current training method of the sample recognition model is generally to train the model by carrying the historical samples with labels, and identify the new samples through the trained model to obtain the category of the samples; however, if carrying The historical samples of the label are less, which will lead to overfitting of the trained model, resulting in a lower accuracy of sample recognition of the model. Contents of the invention [0004] Based on this, it is necessar...
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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/214G06F18/24Y02D10/00
Inventor 李超孙艺芙蓝利君郭清宇赵雪尧卢笑王翔
Owner TENCENT TECH (SHENZHEN) CO LTD