Training method of semi-supervised learning model, image processing method and equipment

Abstract

The embodiment of the invention discloses a training method of a semi-supervised learning model, an image processing method and equipment, and can be applied to the field of computer vision in the field of artificial intelligence, and the method comprises the steps: firstly carrying out the prediction of the classification types of a part of unlabeled samples through a trained first semi-supervised learning model, and obtaining a prediction label; judging whether each prediction label is correct or not in a one-bit labeling manner, if the prediction is correct, obtaining a correct label (namely, a positive label) of the sample, otherwise, eliminating an error label (namely, a negative label) of the sample, and then, in the next training stage, reconstructing a training set (namely, a firsttraining set) by utilizing the information, and training the initial semi-supervised learning model again according to the first training set, so that the prediction accuracy of the model is improved, and due to the fact that the annotator only needs to answer 'Yes' or 'NO' to the prediction label in one-bit annotation, the annotation mode can relieve the manual annotation pressure that a large amount of correct label data is needed in machine learning.

Description

technical field

[0001] The present application relates to the field of machine learning, in particular to a training method for a semi-supervised learning model, an image processing method and equipment. Background technique

[0002] Traditional machine learning tasks are divided into unsupervised learning (unlabeled data, such as clustering, anomaly detection, etc.) and supervised learning (labeled data, such as classification, regression, etc.), and semi-supervised learning (semi-supervised learning, SSL) It is a key issue in the field of pattern recognition and machine learning, and belongs to a learning method that combines supervised learning and unsupervised learning. Semi-supervised learning uses a large amount of unlabeled data and a part of labeled data for pattern recognition.

[0003] The label acquisition of data in real scenarios is often very expensive. However, the existing semi-supervised learning model has certain requirements for the number of labeled data...

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