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Denoising method and device, computer equipment, storage medium and model training method

A computer program and model technology, applied in the field of machine learning, can solve the problem of low accuracy, and achieve the effect of improving accuracy and accuracy

Pending Publication Date: 2020-03-27
上海眼控科技股份有限公司
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Problems solved by technology

[0005] Based on this, it is necessary to provide a noise removal method, device, computer equipment, storage medium and model training method for noise samples in view of the technical problem of low accuracy when correcting wrong labels in traditional technologies

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  • Denoising method and device, computer equipment, storage medium and model training method
  • Denoising method and device, computer equipment, storage medium and model training method

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[0045] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0046] In one embodiment, such as figure 1 As shown, a noise removal method for noise samples is provided, the method includes the following steps:

[0047] S110. Acquire a noise sample sequence and first label data corresponding to the noise sample sequence.

[0048] Among them, the noise sample sequence means that the sample labels used in training the model are not completely accurate, and the labels of some samples have wrong sample sequences. The noise sample sequence may be the image sample set used for image recognition model training, or the speech samples used b...

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Abstract

The invention relates to a noise sample denoising method and device, computer equipment, a storage medium and a model training method. The noise sample denoising method comprises the steps: firstly obtaining a noise sample sequence and first label data corresponding to the noise sample sequence; then, correcting the first label data through a transfer matrix to obtain corrected second label data,thereby improving the accuracy of the label data; and therefore, according to the noise sample sequence, the first label data and the second label data, generating a sample sequence with correct labels through a conditional variation auto-encoder model, further improving the proportion of accurate label samples in the noise sample sequence, and improving the accuracy of the model.

Description

technical field [0001] The present application relates to the field of machine learning, in particular to a noise removal method, device, computer equipment, storage medium and model training method for noise samples. Background technique [0002] In the field of machine learning, a common type of work is to use labeled data to train neural networks for classification, regression, or other purposes. This method of training model learning rules is generally called supervised learning. In supervised learning, in order to obtain a good learning effect, in addition to the high requirement for the number of labeled training data, the quality of the label corresponding to the training data is also crucial to the learning effect. If the wrong labeled data is used for learning, it is impossible to train an effective predictive model. [0003] Usually, the noise sample content can be reduced by noise sample denoising method. The noise sample denoising method refers to learning a de...

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Application Information

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IPC IPC(8): G06K9/40G06K9/62G06N3/04
CPCG06V10/30G06N3/045G06F18/217
Inventor 周康明杭金婷
Owner 上海眼控科技股份有限公司
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