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Small sample generation method and device based on generative adversarial network

A technology against samples and small samples, applied in the field of deep learning, can solve problems such as difficult or unobtainable target sample data

Active Publication Date: 2019-01-11
NAT COMP NETWORK & INFORMATION SECURITY MANAGEMENT CENT +1
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  • Application Information

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Problems solved by technology

However, some specific types of target sample data are often difficult or impossible to obtain

Method used

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  • Small sample generation method and device based on generative adversarial network
  • Small sample generation method and device based on generative adversarial network

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Embodiment Construction

[0054] Preferred embodiments of the present invention are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention, and are not intended to limit the protection scope of the present invention.

[0055] See attached figure 1 , figure 1 The main steps of the small sample generation method based on the adversarial generative network in this embodiment are exemplarily shown. Such as figure 1 As shown, the small sample generation method based on the confrontation generation network in this embodiment may include the following steps:

[0056] Step S101: Obtain random noise conforming to the preset data distribution and label information corresponding to the preset small sample type. Specifically, the random noise in this embodiment may be a random number conforming to (-1,1) uniform distribution. The small sample type can be image samp...

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Abstract

The invention relates to the technical field of deep learning, in particular to a small sample generation method and device based on a generative adversarial network, aiming at solving the technical problem of how to generate sample data by using the generative adversarial network under the condition of a small amount of sample data. For this purpose, the small sample generation method based on the generative adversarial network provided by the present invention can generate samples corresponding to small sample types based on the generative adversarial network and according to random noise and label information. In this process, the invention adopts the methods of migration learning and batch training to train the generative adversarial network, so that the generative adversarial networkcan be effectively migrated and applied to the generative adversarial network sample generation task of a small number of samples.

Description

technical field [0001] The present invention relates to the technical field of deep learning, in particular to a small sample generation method and device based on an adversarial generation network. Background technique [0002] The neural network model built based on deep learning technology usually needs to use a large amount of sample data to complete the model training. However, some specific types of target sample data are often difficult or impossible to obtain. For example, facial expression data and special image data, etc. At present, specific types of target sample data can be generated using GAN technology, but GAN also needs to use a large amount of sample data to complete network training before generating more accurate target sample data. Contents of the invention [0003] In order to solve the above-mentioned problems in the prior art, that is, to solve the technical problem of how to use the generative adversarial network to generate sample data in the ca...

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Application Information

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IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/08G06N3/045
Inventor 李扬曦杜翠兰井雅琪佟玲玲张翠胡卫明李文娟薛建明
Owner NAT COMP NETWORK & INFORMATION SECURITY MANAGEMENT CENT
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