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

Abstract

The invention discloses a small sample target identification method based on a feature subspace generative adversarial network. The method comprises the steps of firstly obtaining features used for training the generative adversarial network, then training the generative adversarial network by using recombined feature data, generating new features by using the trained generative adversarial network to expand small sample data, and finally training a target recognition network by using the expanded data. The key of the method is to simulate an enhancement strategy of underwater images which aredegraded due to different factors by using the characteristics of generative adversarial network data driving and relatively strong imitation capability. And the comprehensiveness and robustness of learning are enhanced by using a multi-branch structure of the attribute branches and the discrimination branches.

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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