Labeled sample determination method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a labeled sample determination method and device based on artificial intelligence, equipment and a storage medium, and the method comprises the steps: obtaining a sample pair which comprises an unlabeled sample and a labeled sample; taking the unlabeled sample and the labeled sample in the sample pair as two paths of inputs of a sample evaluation model respectively to obtain an output result of the sample evaluation model; wherein the sample evaluation model is used for measuring the similarity between two paths of input samples; determining the availability of unlabeled samples in the sample pair according to the output result; and when the availability meets a preset condition, determining an unlabeled sample in the sample pair as a to-be-labeledsample. According to the method, paired learning is introduced into a sample selection process, feature extraction and learning are carried out on an unlabeled sample and a labeled sample by utilizing a sample evaluation model when the labeling value of the unlabeled sample is measured, and the labeling value of the unlabeled sample is measured based on the inter-domain difference of the unlabeled sample and the labeled sample.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence (AI), in particular to a method, device, device and storage medium for determining labeled samples based on artificial intelligence. Background technique [0002] With the rapid development of machine learning technology, deep learning networks have been widely used in various industries. At present, many deep learning networks are trained based on supervised learning algorithms. In this case, the more training samples used to train the deep learning network, the better the model performance of the correspondingly trained deep learning network will be. However, in practical applications, it is difficult to obtain labeled samples, and experts in related fields are required to manually label them, which requires high time and economic costs. [0003] In order to use fewer training samples to train a deep learning network with better model performance, active learning (Acti...

AI Technical Summary

Problems solved by technology

However, this active learning method only focuses on the labeling value of a single unlabeled sample, which does not consider the overall feature distribution of the training sample set composed of these selected samples, often resulting in the selection of unlabeled samples (for The overall distribution characteristics of the training sample set composed of marked) and marked samples are not sufficiently dispersed; using this training sample set to train the deep learning network is difficult to make the deep learning network comprehensively learn various features, which is very important for the deep learning network. Model performance can have a large impact

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