WO2022057691A1 - Gaussian distribution data adjustment method based on improved gan - Google Patents

Gaussian distribution data adjustment method based on improved gan Download PDF

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WO2022057691A1
WO2022057691A1 PCT/CN2021/117026 CN2021117026W WO2022057691A1 WO 2022057691 A1 WO2022057691 A1 WO 2022057691A1 CN 2021117026 W CN2021117026 W CN 2021117026W WO 2022057691 A1 WO2022057691 A1 WO 2022057691A1
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朱锦雷
井焜
许野平
张传锋
刘辰飞
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神思电子技术股份有限公司
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  • the invention relates to a Gaussian distribution data adjustment method based on an improved GAN network, and belongs to the technical field of machine learning.
  • Generative Adversarial Networks (GAN, Generative Adversarial Networks) is a deep learning model and one of the most promising methods for unsupervised learning on complex distributions in recent years. The model produces fairly good outputs through mutual game learning of (at least) two modules in the framework: Generative Model and Discriminative Model.
  • the technical problem to be solved by the present invention is to provide a Gaussian distribution data adjustment method based on an improved GAN network.
  • the generated data output by the GAN network conforms to the Gaussian distribution law, and also satisfies that the characteristics of the generated data and the real data are consistent.
  • the technical solution adopted in the present invention is: a Gaussian distribution data adjustment method based on an improved GAN network, comprising the following steps:
  • the data that natural random number, artificial setting value or other systems are produced are transmitted to the data generation network, and the data generation network generates data according to the input data output;
  • step S02 the generation data of step S01, the real data measuring the approximation of the generated data are transmitted to the data discrimination network, and the data discrimination network obtains the loss Loss1 according to the approximation of the generated data and the real data;
  • the n data generated in batches by the data generation network are transmitted to the Gaussian distribution deviation judgment network, and the Gaussian distribution deviation judgment network uses the central limit theorem to count the distribution of the n data generated in batches, and calculate the distance between the distribution and the normal Gaussian distribution , so as to get the loss Loss2;
  • the weighted sum of the loss Loss2 and the loss Loss1 as the overall loss of the data generation network, use the overall loss to train the image generation network, and the trained data generation network adjusts the input data in line with the Gaussian distribution.
  • the image generating network, the image discriminating network, and the Gaussian distribution deviation judging network are sequentially trained or counted, and the other networks remain unchanged when one of them is trained or counted.
  • step S03 the mean square error of the distribution of the n pieces of data generated in batches and the normal Gaussian distribution is calculated as the loss Loss2.
  • Loss Loss2+ ⁇ Loss1, where ⁇ is an adjustable weighting coefficient.
  • the present invention is used to improve the Gaussian distribution data adjustment of the GAN network, and the randomly generated data is distributed in a specified manner, no matter how the original real data is distributed.
  • the GAN network only changes the distribution of the data, not the data itself, so the generated data is consistent with the real data in characteristics.
  • the present invention is described by taking Gaussian distribution as an example, and other statistical distribution laws of data can also be used for directional normalization, so as to meet the expected requirements of data generation.
  • Figure 1 is a flow chart of the method.
  • This embodiment discloses a Gaussian distribution data adjustment method based on an improved GAN network, as shown in FIG. 1 , including the following steps:
  • the data AZi is transmitted to the data generation network, after the data generation network carries out the corresponding calculation to the input data AZi, the output generation data;
  • the data AZi represents a natural random number, and can also be manually set or data generated by other systems, such as data or images collected by a transmitter;
  • step S02 the generation data of step S01, the real data BZi measuring the approximation of the generated data are transmitted to the data discrimination network, and the data discrimination network obtains the loss Loss1 according to the approximation of the generated data and the real data;
  • the n data generated in batches by the data generation network are transmitted to the Gaussian distribution deviation judgment network, and the Gaussian distribution deviation judgment network uses the central limit theorem to count the distribution of the n data generated in batches, and calculate the distance between the distribution and the normal Gaussian distribution , so as to get the loss Loss2;
  • the central limit theorem shows that when n is large, the sum of independent random variables approximately obeys the normal distribution N(n ⁇ , n ⁇ 2 ), therefore, the target normal distribution function is N(n ⁇ , n ⁇ 2 ), this method
  • the key problem to be solved is to make the approximation of the target normal function as high as possible when n is certain.
  • the weighted sum of the loss Loss2 and the loss Loss1 as the overall loss of the data generation network, use the overall loss to train the image generation network, and the trained data generation network adjusts the input data in line with the Gaussian distribution.
  • the weighted summation of the loss Loss2 and the loss Loss1 is realized by the gating unit, specifically, the weighting coefficient ⁇ and the loss Loss1 are multiplied by the AND gate, and the loss Loss2 and ⁇ Loss1 are added by the OR gate.
  • the image generating network, the image discriminating network, and the Gaussian distribution deviation judging network are sequentially trained or counted, and the other networks remain unchanged during one training or statistics.
  • step S03 the distribution of the n pieces of data generated in batches and the normal Gaussian distribution are calculated as the mean square error as the loss Loss2.
  • Loss1 is the output of the data discrimination network, that is, the characteristic distance between the generated data and the real data (such as Euclidean distance, cosine distance, etc.);
  • the training is divided into two stages Execute, when training the discriminant network, assuming that the parameters of the generation network remain unchanged, generate batch data as negative samples, and real data as positive samples to train the network; when training the generation network, the discriminant network uses the trained model. The feature distance between the generated data and the real data is judged (ie Loss1).
  • GAN network other aspects of the GAN network are the same as the general GAN network principle, and the image generation network, image discrimination network, and Gaussian distribution deviation judgment network can still be common VGG, RESNET, etc. or self-defined networks.
  • This embodiment is used to improve the Gaussian distribution data adjustment of the GAN network, and the randomly generated data is distributed in a specified manner, no matter how the original real data is distributed.
  • the GAN network only changes the distribution of the data, not the data itself, so the generated data is consistent with the real data in characteristics.
  • the present invention is described by taking Gaussian distribution as an example, and other statistical distribution laws of data can also be used for directional normalization, so as to meet the expected requirements of data generation.

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Abstract

A gaussian distribution data adjustment method based on an improved GAN. Gaussian distribution data adjustment is performed by means of an improved GAN, and randomly generated data is distributed in a specified mode regardless of the distribution of original real data. However, the GAN only changes the distribution of the data and does not change the data itself, so that the generated data is consistent with the real data in feature. According to the present method, Gaussian distribution is taken as an example for explanation, and other data statistical distribution rules can also be used for directional standardization, so as to meet data generation expectation requirements.

Description

一种基于改进GAN网络的高斯分布数据调整方法A Gaussian Distribution Data Adjustment Method Based on Improved GAN Network 技术领域technical field
本发明涉及一种基于改进GAN网络的高斯分布数据调整方法，属于机器学习技术领域。The invention relates to a Gaussian distribution data adjustment method based on an improved GAN network, and belongs to the technical field of machine learning.
背景技术Background technique
生成式对抗网络(GAN,Generative Adversarial Networks)是一种深度学习模型，是近年来复杂分布上无监督学习最具前景的方法之一。模型通过框架中(至少)两个模块：生成模型(Generative Model)和判别模型(Discriminative Model)的互相博弈学习产生相当好的输出。Generative Adversarial Networks (GAN, Generative Adversarial Networks) is a deep learning model and one of the most promising methods for unsupervised learning on complex distributions in recent years. The model produces fairly good outputs through mutual game learning of (at least) two modules in the framework: Generative Model and Discriminative Model.
我们经常需要GAN网络生成的数据与真实数据一致，但同时也按一定的期望规律分布，即对生成数据的导向做一定的调整。但是目前还没有通过GAN网络对生成数据的导向做一定调整的方案。We often need the data generated by the GAN network to be consistent with the real data, but also distributed according to a certain expected law, that is, to make certain adjustments to the orientation of the generated data. However, there is currently no plan to adjust the orientation of the generated data through the GAN network.
发明内容SUMMARY OF THE INVENTION
本发明要解决的技术问题是提供一种基于改进GAN网络的高斯分布数据调整方法，通过GAN网络输出的生成数据符合高斯分布规律，同时也满足生成数据与真实数据在特征上是一致的。The technical problem to be solved by the present invention is to provide a Gaussian distribution data adjustment method based on an improved GAN network. The generated data output by the GAN network conforms to the Gaussian distribution law, and also satisfies that the characteristics of the generated data and the real data are consistent.
为了解决所述技术问题，本发明采用的技术方案是：一种基于改进GAN网络的高斯分布数据调整方法，包括以下步骤：In order to solve the technical problem, the technical solution adopted in the present invention is: a Gaussian distribution data adjustment method based on an improved GAN network, comprising the following steps:
S01)、将自然随机数、人工设定值或者其他系统产生的数据传输 至数据生成网络，数据生成网络根据输入数据输出生成数据；S01), the data that natural random number, artificial setting value or other systems are produced are transmitted to the data generation network, and the data generation network generates data according to the input data output;
S02)、步骤S01的生成数据、衡量生成数据近似性的真实数据传输至数据判别网络，数据判别网络根据生成数据与真实数据的近似性得到损失Loss1；S02), the generation data of step S01, the real data measuring the approximation of the generated data are transmitted to the data discrimination network, and the data discrimination network obtains the loss Loss1 according to the approximation of the generated data and the real data;
S03)、数据生成网络批量生成的n个数据传输至高斯分布偏差判断网络，高斯分布偏差判断网络使用中心极限定理统计批量生成的n个数据的分布，并计算该分布与正态高斯分布的距离，从而得出损失Loss2；S03), the n data generated in batches by the data generation network are transmitted to the Gaussian distribution deviation judgment network, and the Gaussian distribution deviation judgment network uses the central limit theorem to count the distribution of the n data generated in batches, and calculate the distance between the distribution and the normal Gaussian distribution , so as to get the loss Loss2;
S04)、损失Loss2与损失Loss1加权求和，作为数据生成网络的整体损失，利用该整体损失对图像生成网络进行训练，训练后的数据生成网络对输入数据做符合高斯分布的调整。S04), the weighted sum of the loss Loss2 and the loss Loss1, as the overall loss of the data generation network, use the overall loss to train the image generation network, and the trained data generation network adjusts the input data in line with the Gaussian distribution.
进一步的，图像生成网络、图像判别网络、高斯分布偏差判断网络依次进行训练或统计，其中一个训练或统计时，其他网络保持不变。Further, the image generating network, the image discriminating network, and the Gaussian distribution deviation judging network are sequentially trained or counted, and the other networks remain unchanged when one of them is trained or counted.
进一步的，步骤S03中，将批量生成的n个数据的分布与正态高斯分布求均方差作为损失Loss2。Further, in step S03, the mean square error of the distribution of the n pieces of data generated in batches and the normal Gaussian distribution is calculated as the loss Loss2.
进一步的，数据生成网络的整体损失设为Loss，则Loss＝Loss2+λLoss1，其中λ为可调的加权系数。Further, the overall loss of the data generation network is set to Loss, then Loss=Loss2+λLoss1, where λ is an adjustable weighting coefficient.
本发明的有益效果：本发明用于改进GAN网络的高斯分布数据调整，随机生成的数据即按指定方式进行分布，无论原始真实数据如何分布。但是GAN网络只变化数据的分布，不改变数据本身，因此生成数据与真实数据在特征上是一致的。本发明以高斯分布为例进行说明，也可用其它数据统计分布规律来做定向规范化，以满足数据生成 期望要求。Beneficial effects of the present invention: the present invention is used to improve the Gaussian distribution data adjustment of the GAN network, and the randomly generated data is distributed in a specified manner, no matter how the original real data is distributed. However, the GAN network only changes the distribution of the data, not the data itself, so the generated data is consistent with the real data in characteristics. The present invention is described by taking Gaussian distribution as an example, and other statistical distribution laws of data can also be used for directional normalization, so as to meet the expected requirements of data generation.
附图说明Description of drawings
图1为本方法的流程图。Figure 1 is a flow chart of the method.
具体实施方式detailed description
下面结合附图和具体实施例对本发明作进一步的说明。The present invention will be further described below with reference to the accompanying drawings and specific embodiments.
实施例1Example 1
本实施例公开一种基于改进GAN网络的高斯分布数据调整方法，如图1所示，包括以下步骤：This embodiment discloses a Gaussian distribution data adjustment method based on an improved GAN network, as shown in FIG. 1 , including the following steps:
S01)、将数据AZi传输至数据生成网络，数据生成网络对输入数据AZi进行相应的计算后，输出生成数据；S01), the data AZi is transmitted to the data generation network, after the data generation network carries out the corresponding calculation to the input data AZi, the output generation data;
本实施例中，数据AZi代表自然随机数，也可以是人工设定或者其他系统产生的数据，如传输器采集的数据或者图像等；In this embodiment, the data AZi represents a natural random number, and can also be manually set or data generated by other systems, such as data or images collected by a transmitter;
S02)、步骤S01的生成数据、衡量生成数据近似性的真实数据BZi传输至数据判别网络，数据判别网络根据生成数据与真实数据的近似性得到损失Loss1；S02), the generation data of step S01, the real data BZi measuring the approximation of the generated data are transmitted to the data discrimination network, and the data discrimination network obtains the loss Loss1 according to the approximation of the generated data and the real data;
S03)、数据生成网络批量生成的n个数据传输至高斯分布偏差判断网络，高斯分布偏差判断网络使用中心极限定理统计批量生成的n个数据的分布，并计算该分布与正态高斯分布的距离，从而得出损失Loss2；S03), the n data generated in batches by the data generation network are transmitted to the Gaussian distribution deviation judgment network, and the Gaussian distribution deviation judgment network uses the central limit theorem to count the distribution of the n data generated in batches, and calculate the distance between the distribution and the normal Gaussian distribution , so as to get the loss Loss2;
中心极限定理表明，当n很大时，相互独立的随机变量之和近似地服从正态分布N(nμ，nσ 2)，因此，目标正态分布函数就是N(nμ，nσ 2)，本方法解决的关键问题是在n一定的情况下，其与目标 正太函数的近似度尽可能高。 The central limit theorem shows that when n is large, the sum of independent random variables approximately obeys the normal distribution N(nμ, nσ 2 ), therefore, the target normal distribution function is N(nμ, nσ 2 ), this method The key problem to be solved is to make the approximation of the target normal function as high as possible when n is certain.
S04)、损失Loss2与损失Loss1加权求和，作为数据生成网络的整体损失，利用该整体损失对图像生成网络进行训练，训练后的数据生成网络对输入数据做符合高斯分布的调整。S04), the weighted sum of the loss Loss2 and the loss Loss1, as the overall loss of the data generation network, use the overall loss to train the image generation network, and the trained data generation network adjusts the input data in line with the Gaussian distribution.
本实施例中，通过门控单元实现损失Loss2与损失Loss1加权求和，具体是通过与门实现加权系数λ与损失Loss1相乘，通过或门实现损失Loss2与λLoss1相加。In this embodiment, the weighted summation of the loss Loss2 and the loss Loss1 is realized by the gating unit, specifically, the weighting coefficient λ and the loss Loss1 are multiplied by the AND gate, and the loss Loss2 and λLoss1 are added by the OR gate.
本实施例中，图像生成网络、图像判别网络、高斯分布偏差判断网络依次进行训练或统计，其中一个训练或统计时，其他网络保持不变。In this embodiment, the image generating network, the image discriminating network, and the Gaussian distribution deviation judging network are sequentially trained or counted, and the other networks remain unchanged during one training or statistics.
步骤S03中，将批量生成的n个数据的分布与正态高斯分布求均方差作为损失Loss2。In step S03, the distribution of the n pieces of data generated in batches and the normal Gaussian distribution are calculated as the mean square error as the loss Loss2.
数据判别网络得到损失Loss1的过程为：Loss1是数据判别网络的输出，即生成数据与真实数据之间的特征距离(如欧式距离、余弦距离等)；根据GAN基本理论，训练分为两个阶段执行，其中训练判别网络时，假定生成网络参数不变，生成批量数据作为负样本，真实数据作为正样本，对网络进行训练；其中训练生成网络时，判别网络使用已训练好的模型，对输入的生成数据、真实数据之间的特征距离进行判断(即Loss1)。The process of obtaining the loss Loss1 by the data discrimination network is: Loss1 is the output of the data discrimination network, that is, the characteristic distance between the generated data and the real data (such as Euclidean distance, cosine distance, etc.); According to the basic theory of GAN, the training is divided into two stages Execute, when training the discriminant network, assuming that the parameters of the generation network remain unchanged, generate batch data as negative samples, and real data as positive samples to train the network; when training the generation network, the discriminant network uses the trained model. The feature distance between the generated data and the real data is judged (ie Loss1).
数据生成网络的整体损失设为Loss，则Loss＝Loss2+λLoss1，其中λ为可调的加权系数，即Loss2与Loss1的权重可调。The overall loss of the data generation network is set to Loss, then Loss=Loss2+λLoss1, where λ is an adjustable weighting coefficient, that is, the weights of Loss2 and Loss1 are adjustable.
本实施例中，GAN网络的其他方面与通用GAN网络原理相同，图 像生成网络、图像判别网络、高斯分布偏差判断网络仍可以是常见的VGG、RESNET等或自定义的网络。In this embodiment, other aspects of the GAN network are the same as the general GAN network principle, and the image generation network, image discrimination network, and Gaussian distribution deviation judgment network can still be common VGG, RESNET, etc. or self-defined networks.
本实施例用于改进GAN网络的高斯分布数据调整，随机生成的数据即按指定方式进行分布，无论原始真实数据如何分布。但是GAN网络只变化数据的分布，不改变数据本身，因此生成数据与真实数据在特征上是一致的。本发明以高斯分布为例进行说明，也可用其它数据统计分布规律来做定向规范化，以满足数据生成期望要求。This embodiment is used to improve the Gaussian distribution data adjustment of the GAN network, and the randomly generated data is distributed in a specified manner, no matter how the original real data is distributed. However, the GAN network only changes the distribution of the data, not the data itself, so the generated data is consistent with the real data in characteristics. The present invention is described by taking Gaussian distribution as an example, and other statistical distribution laws of data can also be used for directional normalization, so as to meet the expected requirements of data generation.
以上描述的仅是本发明的基本原理和优选实施例，本领域技术人员根据本发明做出的改进和替换，属于本发明的保护范围。The above descriptions are only the basic principles and preferred embodiments of the present invention, and improvements and substitutions made by those skilled in the art according to the present invention belong to the protection scope of the present invention.

Claims (4)

  1. 一种基于改进GAN网络的高斯分布数据调整方法，其特征在于：包括以下步骤：A Gaussian distribution data adjustment method based on an improved GAN network, characterized in that it comprises the following steps:
    S01)、将自然随机数、人工设定值或者其他系统产生的数据传输至数据生成网络，数据生成网络根据输入数据输出生成数据；S01), the data generated by natural random numbers, artificial setting values or other systems are transmitted to the data generation network, and the data generation network outputs the generated data according to the input data;
    S02)、步骤S01的生成数据、衡量生成数据近似性的真实数据传输至数据判别网络，数据判别网络根据生成数据与真实数据的近似性得到损失Loss1；S02), the generation data of step S01, the real data measuring the approximation of the generated data are transmitted to the data discrimination network, and the data discrimination network obtains the loss Loss1 according to the approximation of the generated data and the real data;
    S03)、数据生成网络批量生成的n个数据传输至高斯分布偏差判断网络，高斯分布偏差判断网络使用中心极限定理统计批量生成的n个数据的分布，并计算该分布与正态高斯分布的距离，从而得出损失Loss2；S03), the n data generated in batches by the data generation network are transmitted to the Gaussian distribution deviation judgment network, and the Gaussian distribution deviation judgment network uses the central limit theorem to count the distribution of the n data generated in batches, and calculate the distance between the distribution and the normal Gaussian distribution , so as to get the loss Loss2;
    S04)、损失Loss2与损失Loss1加权求和，作为数据生成网络的整体损失，利用该整体损失对图像生成网络进行训练，训练后的数据生成网络对输入数据做符合高斯分布的调整。S04), the weighted sum of the loss Loss2 and the loss Loss1, as the overall loss of the data generation network, use the overall loss to train the image generation network, and the trained data generation network adjusts the input data in line with the Gaussian distribution.
  2. 根据权利要求1所述的基于改进GAN网络的高斯分布数据调整方法，其特征在于：图像生成网络、图像判别网络、高斯分布偏差判断网络依次进行训练或统计，其中一个训练或统计时，其他网络保持不变。The Gaussian distribution data adjustment method based on an improved GAN network according to claim 1, characterized in that: the image generation network, the image discrimination network, and the Gaussian distribution deviation judgment network are trained or counted in sequence, and when one is trained or counted, the other networks constant.
  3. 根据权利要求1所述的基于改进GAN网络的高斯分布数据调整方法，其特征在于：步骤S03中，将批量生成的n个数据的分布与正态高斯分布求均方差作为损失Loss2。The Gaussian distribution data adjustment method based on the improved GAN network according to claim 1, characterized in that: in step S03, the distribution of the n pieces of data generated in batches and the normal Gaussian distribution are calculated as the mean square error as the loss Loss2.
  4. 根据权利要求1所述的基于改进GAN网络的高斯分布数据调 整方法，其特征在于：数据生成网络的整体损失设为Loss，则Loss＝Loss2+λLoss1，其中λ为可调的加权系数。The Gaussian distribution data adjustment method based on improved GAN network according to claim 1 is characterized in that: the overall loss of the data generation network is set to Loss, then Loss=Loss2+λLoss1, wherein λ is an adjustable weighting coefficient.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109146988A (en) * 2018-06-27 2019-01-04 南京邮电大学 Non-fully projection CT image rebuilding method based on VAEGAN
CN109446339A (en) * 2018-10-11 2019-03-08 广东工业大学 A kind of knowledge mapping representation method based on multicore Gaussian Profile
US20190378263A1 (en) * 2018-06-08 2019-12-12 Industrial Technology Research Institute Industrial image inspection method and system and computer readable recording medium
CN112116073A (en) * 2020-09-18 2020-12-22 神思电子技术股份有限公司 Gaussian distribution data adjusting method based on improved GAN network

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190378263A1 (en) * 2018-06-08 2019-12-12 Industrial Technology Research Institute Industrial image inspection method and system and computer readable recording medium
CN109146988A (en) * 2018-06-27 2019-01-04 南京邮电大学 Non-fully projection CT image rebuilding method based on VAEGAN
CN109446339A (en) * 2018-10-11 2019-03-08 广东工业大学 A kind of knowledge mapping representation method based on multicore Gaussian Profile
CN112116073A (en) * 2020-09-18 2020-12-22 神思电子技术股份有限公司 Gaussian distribution data adjusting method based on improved GAN network

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