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Enhanced generative countermeasure network and target sample identification method

A recognition method and enhanced technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve problems such as model failure, difficult GAN training, model collapse, etc., to achieve the effect of convenient training, easy training, and accurate recognition

Active Publication Date: 2018-12-21
SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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AI Technical Summary

Problems solved by technology

[0004] The existing technology mainly has the following disadvantages: ① Deep learning models, especially the widely used deep convolutional neural network models, need to rely on a large number of training samples to show their advantages
However, the existing generative confrontation network (GAN) models usually have the following problems: (a) GAN is difficult to train, especially under the condition of complex distribution of multi-category data; (b) GAN is easy to cause model collapse, which leads to failure of the raw model

Method used

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Embodiment Construction

[0024] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0025] see figure 1 , figure 1 It is a schematic structural diagram of the enhanced generative confrontation network of the first embodiment of the present invention. The enhanced generative confrontation network of the embodiment of the present invention includes an enhanced generator and an enhanced discriminator. The enhanced generator will obtain the initial data to generate data through the anti-pooling layer, linear correction, and filtering (Filtering) layers, and provide Generate data to the enhanced discriminator, and the enhanced discriminator processes the data and feeds back the class...

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Abstract

The invention relates to the technical field of computer application, in particular to an enhanced generated countermeasure network and a target sample identification method. At least one enhance generator and at least one enhanced discriminator are provide for in that enhanced generated countermeasure network of the present invention, the enhanced generator processes the obtained initial data togenerate data, the enhanced discriminator comprises a rolled base layer, a base capsule layer, a convolution capsule layer and a classification capsule layer, wherein the rolled base layer, the base capsule layer, the convolution capsule layer and the classification capsule layer are sequentially connected.

Description

technical field [0001] The invention relates to the technical field of computer applications, in particular to an enhanced generative confrontation network and a target sample identification method. Background technique [0002] Generative Adversarial Networks (GAN, Generative Adversarial Networks) have received extensive attention and applications in unsupervised learning of complex distributions in recent years. A Generative Adversarial Network (GAN) is a deep learning model. The model consists of two modules: the generator model (G) and the discriminator model (D). GAN produces a fairly good output through the mutual game learning of the generator and the discriminator. The generator and discriminator are usually composed of multi-layer networks containing convolutional and / or fully connected layers. The generator and discriminator must be differentiable, but not necessarily directly invertible. The training goal of GAN is to obtain the parameters that maximize the cla...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/241G06F18/214
Inventor 王书强申妍燕张文勇
Owner SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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