Research on Extended Image Data Set Based on Deep Convolution Generative Adversarial Network

Liu, Zixi; Tang, Ming; Liu, Xiaoyu; Du, Zhixiong; Chen, Weicong

doi:10.1109/itnec48623.2020.9085221

Cited by 7 publications

(5 citation statements)

References 1 publication

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“…Deep Convolutional Generative Adversarial network (DCGAN) [20] originated from game adversarial theory and is an improvement on GAN. It combines CNN with GAN, replacing the multi‐layer perceptron structure in GAN with CNN, thereby improving the sample and convergence speed.…”

Section: Data Augmentation Methods Of Sgdcgan‐dcnnmentioning

confidence: 99%

Individual identification method of little sample radiation source based on SGDCGAN+DCNN

Zhen

Zhang

et al. 2022

IET Communications

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Aiming at the issues of low individual identification accuracy of radiation sources under the condition of little samples, this paper proposes a way of individual identification of radiation source based on strengthening global deep convolutional generative adversarial network (SGDCGAN) and deep convolutional neural network (DCNN) to achieve data augmentation. The method first performs IQ map feature splicing processing on the input signal, and then uses DCNN to automatically obtain the deep essential features of the data. Besides, an adaptive improvement is created to the deep convolutional generative adversarial network, and a self‐attention mechanism is introduced into the discriminator and the generator to reinforce the integrity and authenticity of the generated samples. For the common gradient disappearance during model training, the gradient penalty mechanism and spectral normalization are added to make the training process more stable. Through comparison experiments on the collected ADS‐B signals, the experimental results have demonstrated that when the signal‐to‐noise ratio is 0 dB and the number of original samples in each class is 40, the recognition accuracy is improved by 23.6% after doubling the data. Compared with the DCGAN‐DCNN and GAN‐DCNN methods, the recognition accuracy is improved by 3.5% and 4%.

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Section: Data Augmentation Methods Of Sgdcgan‐dcnnmentioning

confidence: 99%

Individual identification method of little sample radiation source based on SGDCGAN+DCNN

Zhen

Zhang

et al. 2022

IET Communications

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show abstract

“…Therefore, the generator and the discriminator form a competitive relationship [21,22]. When training the generator and the discriminator, one is often fixed first to update the weights of the other network, and so on alternately until the generator and the discriminator reach a dynamic balance, that is, Nash equilibrium [23].…”

Section: The Gan-based Model For Generating Samples Of Abnormal Line-...mentioning

confidence: 99%

“…Sustainability 2022, 14, x FOR PEER REVIEW 6 of 16 [21,22]. When training the generator and the discriminator, one is often fixed first to update the weights of the other network, and so on alternately until the generator and the discriminator reach a dynamic balance, that is, Nash equilibrium [23]. The objective function of this model is as follows:…”

Section: The Gan-based Model For Generating Samples Of Abnormal Line-...mentioning

confidence: 99%

Intelligent Identification of the Line-Transformer Relationship in Distribution Networks Based on GAN Processing Unbalanced Data

et al. 2022

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The wrong line-transformer relationship is one of the main reasons that leads to the failure of the line loss assessment of the distribution network with voltage levels of 10 kV and below. The traditional manual method to verify the line-transformer relationship is time-consuming, labor-intensive and inefficient. At the same time, due to the small sample size of the data with abnormal line-transformer relationship, the unbalanced sample data reduces the accuracy of the artificial intelligence algorithm. To this end, this paper proposes an intelligent identification method for distribution network line-transformer relationship based on Generative Adversarial Networks (GAN) processing unbalanced data. Firstly, perform data preprocessing and feature extraction based on the input power of the distribution line and the power consumption of each distribution transformer; then, build a GAN-based model for expanding the data of only a small number of abnormal line-transformer relationship samples, so as to solve the problem of unbalanced sample data distribution; and finally, establish a support vector machine (SVM) to realize the classification of the line-transformer relationship. The results of the example simulation show that, compared with the traditional Synthetic Minority Oversampling Technique (SMOTE) for processing unbalanced data, the classification effect of the proposed GAN-based data augmentation method has been significantly improved. In addition, the recall rate of the three types of the line-transformer relationship (line hanging error, magnification error and normal) under the line-transformer relationship identification method proposed in this paper is more than 92%, which proves the effectiveness and feasibility of the method.

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“…Based on the idea of game theory, the GAN realizes the learning and fitting of the network to the target sample data distribution through the countermeasure process. At present, the GAN has been successfully applied to many fields, such as handwritten font generation [24], image preprocessing [25], data enhancement [26], and IR [27]. Generally, a GAN consists of a generator unit G and a discriminator unit D. The GAN structure is shown in Figure 1.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

“…The structure of the discriminator network is shown in Figure 5. A fully convolutional neural network architecture was To improve the reconstruction performance, a residual block was placed between each convolutional layer and the deconvolutional layer, which was inspired by the skip connection in ResNet [26]. The residual block could deepen the generator network and help to achieve fast convergence.…”

Section: Proposed Discriminator Networkmentioning

confidence: 99%

Image Reconstruction with Multiscale Interest Points Based on a Conditional Generative Adversarial Network

et al. 2022

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A new image reconstruction (IR) algorithm from multiscale interest points in the discrete wavelet transform (DWT) domain was proposed based on a modified conditional generative adversarial network (CGAN). The proposed IR-DWT-CGAN model generally integrated a DWT module, an interest point extraction module, an inverse DWT module, and a CGAN. First, the image was transformed using the DWT to provide multi-resolution wavelet analysis. Then, the multiscale maxima points were treated as interest points and extracted in the DWT domain. The generator was a U-net structure to reconstruct the original image from a very coarse version of the image obtained from the inverse DWT of the interest points. The discriminator network was a fully convolutional network, which was used to distinguish the restored image from the real one. The experimental results on three public datasets showed that the proposed IR-DWT-CGAN model had an average increase of 2.9% in the mean structural similarity, an average decrease of 39.6% in the relative dimensionless global error in synthesis, and an average decrease of 48% in the root-mean-square error compared with several other state-of-the-art methods. Therefore, the proposed IR-DWT-CGAN model is feasible and effective for image reconstruction with multiscale interest points.

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Research on Extended Image Data Set Based on Deep Convolution Generative Adversarial Network

Cited by 7 publications

References 1 publication

Individual identification method of little sample radiation source based on SGDCGAN+DCNN

Individual identification method of little sample radiation source based on SGDCGAN+DCNN

Intelligent Identification of the Line-Transformer Relationship in Distribution Networks Based on GAN Processing Unbalanced Data

Image Reconstruction with Multiscale Interest Points Based on a Conditional Generative Adversarial Network

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