Small sample target recognition method based on feature subspace generative adversarial network

Abstract

The invention discloses a small-sample target recognition method based on feature subspace generation confrontation network; the invention first obtains the features used for training generation confrontation network, then uses the reorganized feature data to train generation confrontation network, and then uses the trained The generated confrontation network generates new features to expand the small sample data, and finally uses the expanded data to train the target recognition network. The key of the present invention is to imitate the enhancement strategy for underwater images degraded by different factors by using the data-driven characteristics of the generative adversarial network and strong imitation ability. The multi-branch structure of attribute branch and discriminative branch is used to enhance the comprehensiveness and robustness of learning.

Description

technical field [0001] The invention belongs to the field of deep learning and relates to a small-sample target recognition method for generating an adversarial network in a feature subspace. Background technique [0002] Few-shot object recognition is a problem often encountered in the field of deep learning. Although the target recognition method based on deep learning has high recognition accuracy, it has a relatively weak point in the problem of small sample target recognition. Therefore, it is necessary to use the generative confrontation network to expand the small sample data. [0003] Generative Adversarial Network (GAN) is a network structure that is trained against a Generative Network (G) and a Discriminative Network (D). The purpose of the G network is to make the synthesized image as close as possible to the real image, making it indistinguishable for the D network, which aims to distinguish the real image from the synthetic image. GAN has been successfully a...

AI Technical Summary

Problems solved by technology

Although the target recognition method based on deep learning has high recognition accuracy, it has a relatively weak point in the problem of small sample target recognition.

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