An Image Classification Method Based on Unsupervised Domain Adaptation

Abstract

The invention discloses an image classification method based on unsupervised domain adaptation, which includes: selecting a labeled source domain image data set and an unlabeled target domain image data set, and performing data augmentation; constructing an anti-transfer network to reduce source domain images The difference between the conditional distribution of the data set and the target domain image data set; constructing a time series integration network to regularize the prediction results of the image labels in the target domain image data set; combining the confrontation migration network and the time series integration network to construct an image classification model; The final image data set is used as the training set, and the image classification model is trained by means of meta-learning; the trained image classification model is used to identify the target image to be classified and complete the target image classification. In the process of classifying massive image data on the Internet, the present invention greatly reduces manpower labeling data without affecting the accuracy of image classification, and users can quickly and accurately search for desired images from massive image data.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to an image classification method based on unsupervised domain adaptation. Background technique [0002] With the popularization of digital products and smart mobile terminal equipment, the development of storage devices and computer networks, hundreds of millions of massive image data are added to the network every day. These image data contain a lot of valuable information. Utilization is obviously a great waste. However, in the face of vast and huge image databases, how to quickly and accurately classify images and obtain the image results that users want to search has become an urgent problem to be solved in the scientific research and commercial fields. Many artificial intelligence technologies today have made great achievements in the field of classification and recognition of network images, but these artificial intelligence technologies rely on a large number of a...

AI Technical Summary

Problems solved by technology

Many artificial intelligence technologies today have made great achievements in the field of classification and recognition of network images, but these artificial intelligence technologies rely on a large number of annotations, and the process of annotating large amounts of data is very difficult for laborers, and the cost is extremely expensive, even not possible

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