Labeled sample determination method and device, equipment and storage medium

A technology for determining methods and samples, applied to instruments, character and pattern recognition, computing models, etc., can solve problems such as difficult deep learning network comprehensive learning, the overall feature distribution of the training sample set is not considered, and the overall distribution features are not scattered enough to achieve The effect of good model performance
CN110766080AActive Publication Date: 2020-02-07腾讯医疗健康(深圳)有限公司
7 Cites 10 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
腾讯医疗健康(深圳)有限公司
Publication Date
2020-02-07

Smart Images

  • Figure 1
    Figure 1
  • Figure 2
    Figure 2
  • Figure 3
    Figure 3
Patent Text Reader

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.
Need to check novelty before this filing date? Find Prior Art

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More