Model update device and method, data processing device and method, program

Abstract

The present application discloses a model update device and method, data processing device and method, which are used to update the target model in the multi-model system, wherein each model in the multi-model system is pre-trained in different ways for the training data set The obtained model, the model update device includes: a pseudo-label acquisition unit, which uses a calibration model to process the data set to be tested, and uses the processed result as a pseudo-label; the first feature distribution acquisition unit obtains the data set to be tested based on the pseudo-label. Feature distribution; the second feature distribution acquisition unit obtains the feature distribution of the training data set based on the target model; the adjustment unit adjusts the feature space division of the target model based on the feature distribution of the training data set and the feature distribution of the test data set, so that The training data set and the test data set have similar distributions for the feature space division; and an update unit uses the training data set to update the target model based on the adjusted feature space division.

Description

technical field [0001] The present application relates to the field of data processing, in particular to a model updating device and a model updating method for updating a model in data processing, and a data processing device and a data processing method using the model updating device and model updating method. Background technique [0002] With the progress of society and the rapid development of information technology, how to efficiently process massive amounts of information has become particularly important. With the help of computer technology, various data processing methods, such as entity relationship extraction, object recognition, data mining, etc., have become common processing methods. These treatments are usually based on one or more models. For example, in entity relationship extraction, machine learning-based methods extract features from relationship samples, then train models on relationship samples, and use the trained model to identify entity relationsh...

AI Technical Summary

Problems solved by technology

However, in many cases, this assumption is not fully established, resulting in the model obtained based on the training data set is not suitable for the test data set, especially when new data is generated, this problem is particularly prominent

Moreover, it takes a lot of time and manpower to label samples on new data, making it more expensive to update the model

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