Generalized zero sample target classification method based on active learning and variational auto-encoder

Abstract

The invention provides a generalized zero sample target classification method based on active learning and a variational auto-encoder. The method is used for solving the problem of bias caused by loss of unknown class supervision information and the problem of low-dimensional feature aggregation caused by projection from high-dimensional features to low-dimensional space in the prior art, and effectively improving the classification accuracy. The method comprises the following implementation steps: acquiring a training sample set Ptrain and a test sample set Ptest; constructing a generalized zero sample classification model H based on the variational auto-encoder; carrying out iterative training on the variational auto-encoder f and a nonlinear classifier fclassifier in the generalized zero sample classification model H based on the variational auto-encoder; and obtaining a target classification result of a generalized zero sample. The method can be applied to the fields of classification of rare species lacking training data, biomedical image recognition and the like.

Description

technical field [0001] The invention belongs to the technical field of zero-sample image classification, and relates to a generalized zero-sample object classification method, in particular to a generalized zero-sample object classification method based on active learning and variational autoencoders, which can be used for rare species classification and biomedical images identification etc. Background technique [0002] Object classification is one of the main research directions of artificial intelligence. With the vigorous development of artificial intelligence, object classification has been widely used in artificial intelligence fields such as defect detection, unmanned driving, and medical diagnosis. The current research on object classification mainly focuses on the classification of images. But with the rapid development of social networks and social labeling systems, new labels and concepts are constantly emerging, followed by the problem of how people use these ne...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to overcome the above-mentioned defects in the prior art, and propose a generalized zero-sample object classification method based on active learning and variational autoencoder, which is used to solve the generalized zero-sample object classification accuracy rate existing in the prior art lower technical issues

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