Method and device for training supervised machine learning model

Abstract

A method and a device for training a supervised machine learning model are disclosed. The method includes the following steps: generating an artificial image containing a target object; recording annotation data related to the target object in the process of generating the artificial image; using the artificial image as the input data of a model to perform the operation in the model in order to obtain derivation data related to the target object; and comparing the derivation data with the annotation data to determine whether or not to adjust the parameters of the model. Through the method, a lot of manual annotation required in the training process of the model can be omitted.

Description

technical field [0001] The present disclosure generally relates to the technical field of supervised machine learning models, and in particular to methods and apparatuses for training supervised machine learning models. Background technique [0002] Supervised machine learning (supervised machine learning) usually needs to use a large number of training samples to train the model, and determine whether to adjust the parameters of the model according to the comparison between the expected results and the derivation results obtained by the model using the training samples And how to adjust the parameters of the model so that the model can be well adapted to other data (for example, actual application data) other than the training samples. Supervised machine learning models may include, for example, artificial neural networks (eg, convolutional neural networks), decision trees, and the like. [0003] Many different training sample sets or training sample banks have been provid...

AI Technical Summary

Problems solved by technology

The cost of training is high, but the accuracy and efficiency are low

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