Label sample determination method and device, machine readable medium and equipment

Abstract

The invention discloses a labeled sample determination method. The labeled sample determination method comprises the steps of obtaining a pre-trained classification model and a classification target;repeating the following steps to iteratively update the classification model until a preset stop condition is met, and taking the corresponding sample set when the preset stop condition is met as a to-be-labeled sample set; predicting samples in a sample set by utilizing the classification model to obtain a classification score of each sample belonging to each classification target; performing fusion sorting on the classification score of each sample belonging to each classification target to obtain a plurality of fusion sorting results; determining a to-be-labeled sample set from the plurality of fusion sorting results; and updating the classification model by utilizing the to-be-labeled sample set. According to the method, the expert annotation amount required by model training can be remarkably reduced, the labor cost is saved, the benefit of unit annotation is improved, the model is quickly iterated, and the method is different from a single-strategy active learning scheme, so thatthe problem that high-weight samples generated by fusion sorting of a single strategy are omitted is effectively solved.

Description

technical field [0001] The present invention relates to the field of artificial intelligence, in particular to a method, device, machine-readable medium and equipment for determining a labeled sample. Background technique [0002] In recent years, the emerging machine learning and deep learning algorithm models have achieved rapid development in the field of artificial intelligence. Among them, deep learning technology has achieved good results in computer vision, speech recognition, natural language processing and other fields. [0003] Most of the models used by these deep learning technologies are neural networks based on a large number of parameters. In the process of model training, in order to better fit the parameters, a huge amount of labeled data is often required for training. However, in the application of these deep learning algorithms in the industry of subdivided fields, the use of existing public data for modeling often does not achieve optimal results. It is...

AI Technical Summary

Problems solved by technology

In the fields of computer vision, speech recognition, natural language processing and other fields, if you want to obtain new label data, you can only manually label a large number of pictures, voice, and text data to obtain labels, which consumes a lot of time and labor costs.

At the same time, in the process of training the model, too large a training sample set will also lead to longer training time and slower iteration speed.

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