Model training method and device based on active learning and server

Abstract

The embodiment of the invention provides a model training method and device based on active learning, and a server, and the method and device can automatically call a training sample of each application scene indicated by a started training task, and do not need a developer to manually export data for model training. After an initial deep learning network model matched with each application scenein advance is scheduled to perform sample labeling on training samples, an active learning service is scheduled to perform active screening on the labeled training samples, developers do not need to participate in labeling, the sample labeling number is effectively reduced, the active screening sample is calibrated according to a calibration instruction of a user, a training service associated with each application scene is flexibly scheduled to perform model training based on the calibrated active screening sample, and the model is published to a software application program corresponding toeach corresponding application scene; therefore, training services of different application scenarios can be flexibly docked, and automatic labeling, training and service updating of the training process are achieved.

Description

technical field [0001] The present application relates to the technical field of deep learning, in particular, to a model training method, device and server based on active learning. Background technique [0002] Among related technologies, most artificial intelligence technologies are related applications of supervised learning technology in machine learning. The development process of a supervised learning model requires the use of sample data for model training, and then the trained supervised learning model is packaged into an application module for deployment to the application scenario. When there is a certain difference between the sample data collected in the application scenario and the sample data used in training, the supervised learning model will not be able to meet the needs of the application, and it is necessary to re-collect sample data and train again, which will lead to an increase in the cost of the application , the application launch is delayed, and th...

AI Technical Summary

Problems solved by technology

When there is a certain difference between the sample data collected in the application scenario and the sample data used in training, the supervised learning model will not be able to meet the needs of the application, and it is necessary to re-collect sample data and train again, which will lead to an increase in the cost of the application , the application launch is delayed, and the efficiency of re-collecting samples and training again is low, and algorithm engineers with professional knowledge are also required to participate

Application scenarios Due to business changes and condition changes, the accuracy of the supervised learning model cannot meet the application requirements

[0003] The inventors of the present application found that for the deep learning model based on supervised learning, there are subtle differences between the training data and the field data of the application scene, which may cause the model to fail to meet the requirements of the application. Data and retraining the model to improve the ability of the original model to adapt to the field environment. This method is complex to operate and has a low degree of automation. It requires the participation of professional developers, and the work of sample labeling is large. It is often necessary to update the field application system to use the retrained model, leading to low R&D efficiency and even affecting business delivery and launch

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