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

A feature subspace, target recognition technology, applied in the field of deep learning, can solve the problem of small sample target recognition and other problems

Active Publication Date: 2019-10-22
HANGZHOU DIANZI UNIV
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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.

Method used

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

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Experimental program
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Embodiment Construction

[0022] The present invention is further analyzed below in conjunction with specific examples.

[0023] Such as figure 1 As shown in , this experiment uses a set of collected ship target images as the training sample data set. The specific steps in the small sample target recognition task based on the feature subspace generation confrontation network are as follows:

[0024] Step (1), Obtain the features used to train the Generative Adversarial Network

[0025] 1.1 Use the classification network to perform rough training on image sample data.

[0026] Use the convolutional neural network to perform rough training on the image sample data. After training, the weight parameters and bias parameters are W and B respectively;

[0027] 1.2 Use W and B two sets of parameters to map the sample to the feature subspace to obtain feature data.

[0028] Assuming that there are n images in the sample data, the n image sample data is set A, and each image sample is A k (where 1

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06V20/20G06V2201/07G06N3/045
Inventor 陈华杰侯新雨姚勤炜张杰豪韦玉谭刘栋
Owner HANGZHOU DIANZI UNIV
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