Relevant bibliographies by topics / WGAN-GP

Academic literature on the topic 'WGAN-GP'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "WGAN-GP"

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Xu, Jialing, Jingxing He, Jinqiang Gu, Huayang Wu, Lei Wang, Yongzhen Zhu, Tiejun Wang, Xiaoling He, and Zhangyuan Zhou. "Financial Time Series Prediction Based on XGBoost and Generative Adversarial Networks." International Journal of Circuits, Systems and Signal Processing 16 (January 15, 2022): 637–45. http://dx.doi.org/10.46300/9106.2022.16.79.

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Considering the problems of the model collapse and the low forecast precision in predicting the financial time series of the generative adversarial networks (GAN), we apply the WGAN-GP model to solve the gradient collapse. Extreme gradient boosting (XGBoost) is used for feature extraction to improve prediction accuracy. Alibaba stock is taken as the research object, using XGBoost to optimize its characteristic factors, and training the optimized characteristic variables with WGAN-GP. We compare the prediction results of WGAN-GP model and classical time series prediction models, long short term memory (LSTM) and gate recurrent unit (GRU). In the experimental stage, root mean square error (RMSE) is chosen as the evaluation index. The results of different models show that the RMSE of WGAN-GP model is the smallest, which are 61.94% and 47.42%, lower than that of LSTM model and GRU model respectively. At the same time, the stock price data of Google and Amazon confirm the stability of WGAN-GP model. WGAN-GP model can obtain higher prediction accuracy than the classical time series prediction model.
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Yang, Kunlin, and Yang Liu. "Global Ionospheric Total Electron Content Completion with a GAN-Based Deep Learning Framework." Remote Sensing 14, no. 23 (November 29, 2022): 6059. http://dx.doi.org/10.3390/rs14236059.

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The ionosphere serves as a critical medium for radio signal propagation in outer space. A good morphology of the global TEC distribution is very useful for both ionospheric studies and their relative applications. In this work, a deep learning framework was constructed for better spatial estimation in ionospheric TEC. Both the DCGAN and WGAN-GP were considered, and their performances were evaluated with spatial completion for a regional TEC. The performances were evaluated using the correlation coefficient, RMSE, and MAE. Moreover, the IAAC rapid products were used to make comparisons. The results show that both the DCGAN and WGAN-GP outperformed the IAAC CORG rapid products. The spatial TEC estimation clearly goes well with the solar activity trend. The RMSE differences had a maximum of 0.5035 TECu between the results of 2009 and 2014 for the DCGAN and a maximum of 0.9096 TECu between the results of 2009 and 2014 for the WGAN-GP. Similarly, the MAE differences had a maximum of 0.2606 TECu between the results of 2009 and 2014 for DCGAN and a maximum of 0.3683 TECu between the results of 2009 and 2014 for WGAN-GP. The performances of the CORG, DCGAN, and WGAN-GP were also verified for two selected strong geomagnetic storms in 2014 and 2017. The maximum RMSEs were 1.8354 TECu and 2.2437 TECu for the DCGAN and WGAN-GP in the geomagnetic storm on 18 February 2014, respectively, and the maximum RMSEs were 1.3282 TECu and 1.4814 TECu in the geomagnetic storm on 7 September 2017. The GAN-based framework can extract the detailed features of spatial TEC daily morphologies and the responses during geomagnetic storms.
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Qin, Jing, Fujie Gao, Zumin Wang, Lu Liu, and Changqing Ji. "Arrhythmia Detection Based on WGAN-GP and SE-ResNet1D." Electronics 11, no. 21 (October 23, 2022): 3427. http://dx.doi.org/10.3390/electronics11213427.

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A WGAN-GP-based ECG signal expansion and an SE-ResNet1D-based ECG classification method are proposed to address the problem of poor modeling results due to the imbalanced sample distribution of ECG data sets. The network architectures of WGAN-GP and SE-ResNet1D are designed according to the characteristics of ECG signals so that they can be better applied to the generation and classification of ECG signals. First, ECG data were generated using WGAN-GP on the MIT-BIH arrhythmia database to balance the dataset. Then, the experiments were performed using the AAMI category and inter-patient data partitioning principles, and classification experiments were performed using SE-ResNet1D on the imbalanced and balanced datasets, respectively, and compared with three networks, VGGNet, DenseNet and CNN+Bi-LSTM. The experimental results show that using WGAN-GP to balance the dataset can improve the accuracy and robustness of the model classification, and the proposed SE-ResNet1D outperforms the comparison model, with a precision of 95.80%, recall of 96.75% and an F1 measure of 96.27% on the balanced dataset. Our methods have the potential to be a useful diagnostic tool to assist cardiologists in the diagnosis of arrhythmias.
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Arbat, Shivani, Vinodh Kumaran Jayakumar, Jaewoo Lee, Wei Wang, and In Kee Kim. "Wasserstein Adversarial Transformer for Cloud Workload Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12433–39. http://dx.doi.org/10.1609/aaai.v36i11.21509.

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Predictive VM (Virtual Machine) auto-scaling is a promising technique to optimize cloud applications’ operating costs and performance. Understanding the job arrival rate is crucial for accurately predicting future changes in cloud workloads and proactively provisioning and de-provisioning VMs for hosting the applications. However, developing a model that accurately predicts cloud workload changes is extremely challenging due to the dynamic nature of cloud workloads. Long- Short-Term-Memory (LSTM) models have been developed for cloud workload prediction. Unfortunately, the state-of-the-art LSTM model leverages recurrences to predict, which naturally adds complexity and increases the inference overhead as input sequences grow longer. To develop a cloud workload prediction model with high accuracy and low inference overhead, this work presents a novel time-series forecasting model called WGAN-gp Transformer, inspired by the Transformer network and improved Wasserstein-GANs. The proposed method adopts a Transformer network as a generator and a multi-layer perceptron as a critic. The extensive evaluations with real-world workload traces show WGAN- gp Transformer achieves 5× faster inference time with up to 5.1% higher prediction accuracy against the state-of-the-art. We also apply WGAN-gp Transformer to auto-scaling mechanisms on Google cloud platforms, and the WGAN-gp Transformer-based auto-scaling mechanism outperforms the LSTM-based mechanism by significantly reducing VM over-provisioning and under-provisioning rates.
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Duan, Xintao, Baoxia Li, Daidou Guo, Kai Jia, En Zhang, and Chuan Qin. "Coverless Information Hiding Based on WGAN-GP Model." International Journal of Digital Crime and Forensics 13, no. 4 (July 2021): 57–70. http://dx.doi.org/10.4018/ijdcf.20210701.oa5.

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Steganalysis technology judges whether there is secret information in the carrier by monitoring the abnormality of the carrier data, so the traditional information hiding technology has reached the bottleneck. Therefore, this paper proposed the coverless information hiding based on the improved training of Wasserstein GANs (WGAN-GP) model. The sender trains the WGAN-GP with a natural image and a secret image. The generated image and secret image are visually identical, and the parameters of generator are saved to form the codebook. The sender uploads the natural image (disguise image) to the cloud disk. The receiver downloads the camouflage image from the cloud disk and obtains the corresponding generator parameter in the codebook and inputs it to the generator. The generator outputs the same image for the secret image, which realized the same results as sending the secret image. The experimental results indicate that the scheme produces high image quality and good security.
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Han, Baokun, Sixiang Jia, Guifang Liu, and Jinrui Wang. "Imbalanced Fault Classification of Bearing via Wasserstein Generative Adversarial Networks with Gradient Penalty." Shock and Vibration 2020 (July 21, 2020): 1–14. http://dx.doi.org/10.1155/2020/8836477.

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Recently, generative adversarial networks (GANs) are widely applied to increase the amounts of imbalanced input samples in fault diagnosis. However, the existing GAN-based methods have convergence difficulties and training instability, which affect the fault diagnosis efficiency. This paper develops a novel framework for imbalanced fault classification based on Wasserstein generative adversarial networks with gradient penalty (WGAN-GP), which interpolates randomly between the true and generated samples to ensure that the transition region between the true and false samples satisfies the Lipschitz constraint. The process of feature learning is visualized to show the feature extraction process of WGAN-GP. To verify the availability of the generated samples, a stacked autoencoder (SAE) is set to classify the enhanced dataset composed of the generated samples and original samples. Furthermore, the exhibition of the loss curve indicates that WGAN-GP has better convergence and faster training speed due to the introduction of the gradient penalty. Three bearing datasets are employed to verify the effectiveness of the developed framework, and the results show that the proposed framework has an excellent performance in mechanical fault diagnosis under the imbalanced training dataset.
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Fan, Hongwei, Jiateng Ma, Xuhui Zhang, Ceyi Xue, Yang Yan, and Ningge Ma. "Intelligent data expansion approach of vibration gray texture images of rolling bearing based on improved WGAN-GP." Advances in Mechanical Engineering 14, no. 3 (March 2022): 168781322210861. http://dx.doi.org/10.1177/16878132221086132.

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Rolling bearing is one of the components with the high fault rate for rotating machinery. Big data-based deep learning is a hot topic in the field of bearing fault diagnosis. However, it is difficult to obtain the big actual data, which leads to a low accuracy of bearing fault diagnosis. WGAN-based data expansion approach is discussed in this paper. Firstly, the vibration signal is converted into the gray texture image by LBP to build the original data set. The small original data set is used to generate the new big data set by WGAN with GP. In order to verify its effectiveness, MMD is used for the expansion evaluation, and then the effect of the newly generated data on the original data expansion in different proportions is verified by CNN. The test results show that WGAN-GP data expansion approach can generate the high-quality samples, and CNN-based classification accuracy increases from 92.5% to 97.5% before and after the data expansion.
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Chang, Jiaxing, Fei Hu, Huaxing Xu, Xiaobo Mao, Yuping Zhao, and Luqi Huang. "Towards Generating Realistic Wrist Pulse Signals Using Enhanced One Dimensional Wasserstein GAN." Sensors 23, no. 3 (January 28, 2023): 1450. http://dx.doi.org/10.3390/s23031450.

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For the past several years, there has been an increasing focus on deep learning methods applied into computational pulse diagnosis. However, one factor restraining its development lies in the small wrist pulse dataset, due to privacy risks or lengthy experiments cost. In this study, for the first time, we address the challenging by presenting a novel one-dimension generative adversarial networks (GAN) for generating wrist pulse signals, which manages to learn a mapping strategy from a random noise space to the original wrist pulse data distribution automatically. Concretely, Wasserstein GAN with gradient penalty (WGAN-GP) is employed to alleviate the mode collapse problem of vanilla GANs, which could be able to further enhance the performance of the generated pulse data. We compared our proposed model performance with several typical GAN models, including vanilla GAN, deep convolutional GAN (DCGAN) and Wasserstein GAN (WGAN). To verify the feasibility of the proposed algorithm, we trained our model with a dataset of real recorded wrist pulse signals. In conducted experiments, qualitative visual inspection and several quantitative metrics, such as maximum mean deviation (MMD), sliced Wasserstein distance (SWD) and percent root mean square difference (PRD), are examined to measure performance comprehensively. Overall, WGAN-GP achieves the best performance and quantitative results show that the above three metrics can be as low as 0.2325, 0.0112 and 5.8748, respectively. The positive results support that generating wrist pulse data from a small ground truth is possible. Consequently, our proposed WGAN-GP model offers a potential innovative solution to address data scarcity challenge for researchers working with computational pulse diagnosis, which are expected to improve the performance of pulse diagnosis algorithms in the future.
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Sun, Caihao, Xiaohua Zhang, Hongyun Meng, Xianghai Cao, and Jinhua Zhang. "AC-WGAN-GP: Generating Labeled Samples for Improving Hyperspectral Image Classification with Small-Samples." Remote Sensing 14, no. 19 (October 1, 2022): 4910. http://dx.doi.org/10.3390/rs14194910.

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The lack of labeled samples severely restricts the classification performance of deep learning on hyperspectral image classification. To solve this problem, Generative Adversarial Networks (GAN) are usually used for data augmentation. However, GAN have several problems with this task, such as the poor quality of the generated samples and an unstable training process. Thereby, knowing how to construct a GAN to generate high-quality hyperspectral training samples is meaningful for the small-sample classification task of hyperspectral data. In this paper, an Auxiliary Classifier based Wasserstein GAN with Gradient Penalty (AC-WGAN-GP) was proposed. The framework includes AC-WGAN-GP, an online generation mechanism, and a sample selection algorithm. The proposed method has the following distinctive advantages. First, the input of the generator is guided by prior knowledge and a separate classifier is introduced to the architecture of AC-WGAN-GP to produce reliable labels. Second, an online generation mechanism ensures the diversity of generated samples. Third, generated samples that are similar to real data are selected. Experiments on three public hyperspectral datasets show that the generated samples follow the same distribution as the real samples and have enough diversity, which effectively expands the training set. Compared to other competitive methods, the proposed framework achieved better classification accuracy with a small number of labeled samples.
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Lee, Junwon, and Heejo Lee. "Improving SSH detection model using IPA time and WGAN-GP." Computers & Security 116 (May 2022): 102672. http://dx.doi.org/10.1016/j.cose.2022.102672.

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Dissertations / Theses on the topic "WGAN-GP"

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GIANSANTI, VALENTINA. "Integration of heterogeneous single cell data with Wasserstein Generative Adversarial Networks." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/404516.

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Tessuti, organi e organismi sono sistemi biologici complessi, oggetto di studi che mirano alla caratterizzazione dei loro processi biologici. Comprendere il loro funzionamento e la loro interazione in campioni sani e malati consente di interferire, correggere e prevenire le disfunzioni dalle quali si sviluppano possibilmente le malattie. I recenti sviluppi nelle tecnologie di sequenziamento single-cell stanno ampliano la capacità di profilare, a livello di singola cellula, diversi layer molecolari (trascrittoma, genoma, epigenoma, proteoma). Il numero, la grandezza e le diverse modalità dei dataset prodotti è in continua crescita. Ciò spinge allo sviluppo di robusti metodi per l’integrazione di dataset multiomici, che siano essi descrittivi o meno delle stesse cellule. L’integrazione di più fonti di informazione produce una descrizione più ampia e completa dell’intero sistema analizzato. La maggior parte dei sistemi di integrazione disponibili ad oggi consente l’analisi simultanea di un numero limitato di omiche (generalmente due) e richiede conoscenze pregresse riguardo le loro relazioni. Questi metodi spesso impongono la traduzione di una modalità nelle variabili espresse da un altro dato (ad esempio, i picchi di ATAC vengono convertiti in gene activity matrix). Questo step introduce un livello di approssimazione nel dato che potrebbe pregiudicare le analisi svolte in seguito. Da qui nasce MOWGAN (Multi Omic Wasserstein Generative Adversarial Network), un framework basato sul deep-learning, per la simulazione di dati multimodali appaiati in grado di supportare un alto numero di dataset (più di due) e agnostico sulle relazioni che intercorrono tra loro (non viene imposta alcuna assunzione). Ogni modalità viene proiettata in uno spazio descrittivo ridotto, le cui dimensioni sono fissate per tutti i datasets. Questo processo previene la traduzione tra modalità. Le cellule, descritte da vettori nello spazio ridotto, vengono ordinate in base alla prima componente della loro Laplacian Eigenmap. Un regressore Bayesian viene successivamente applicato per selezionare i mini-batch con i quali viene allenata una particolare architettura di deep-learning, la Wasserstein Generative Adversarial Network with gradient penalty. La componente generativa della rete restituisce in uscita un nuovo dataset, appaiato, che viene utilizzato come ponte per il passaggio di informazioni tra i dataset originali. Lo sviluppo di MOWGAN è stato condotto con l’ausilio di dati pubblici per i quali erano disponibili osservazioni di RNA e ATAC sia per le stesse cellule, che per cellule differenti. La valutazione dei risultati è stata condotta sulla base della capacità del dato prodotto di essere integrato con il dato originale. Inoltre, il dato sintetico deve avere informazione condivisa tra le diverse omiche. Questa deve rispettare la natura biologica del dato: le associazioni non devono essere presenti tra entità cellulari rappresentanti tipi cellulari differenti. L’organizzazione del dato in mini-batch consente a MOWGAN di avere una architettura di rete indipendente dal numero di modalità considerate. Infatti, il framework è stato applicato anche per l’integrazione di tre (RNA, ATAC e proteine, RNA ATAC e modificazioni istoniche) e quattro modalità (RNA, ATAC, proteine e modificazioni istoniche). Il rendimento di MOWGAN è stato dunque valutato in termini di scalabilità computazionale (integrazione di molteplici datasets) e significato biologico, essendo quest’ultimo il più importante per non giungere a conclusioni errate nello studio in essere. È stato eseguito un confronto con altri metodi già disponibili in letteratura, riscontrando la maggiore capacità di MOWGAN di creare associazioni inter-modali tra entità cellulari realmente legate. In conclusione, MOWGAN è uno strumento potente per l’integrazione di dati multi-modali in single-cell, che risponde a molte delle problematiche riscontrate nel campo.
Tissues, organs and organisms are complex biological systems. They are objects of many studies aiming at characterizing their biological processes. Understanding how they work and how they interact in healthy and unhealthy samples gives the possibility to interfere, correcting and preventing dysfunctions, possibly leading to diseases. Recent advances in single-cell technologies are expanding our capabilities to profile at single-cell resolution various molecular layers, by targeting the transcriptome, the genome, the epigenome and the proteome. The number of single-cell datasets, their size and the diverse modalities they describe is continuously increasing, prompting the need to develop robust methods to integrate multiomic datasets, whether paired from the same cells or, most challenging, from unpaired separate experiments. The integration of different source of information results in a more comprehensive description of the whole system. Most published methods allow the integration of limited number of omics (generally two) and make assumptions about their inter-relationships. They often impose the conversion of a data modality into the other one (e.g., ATAC peaks converted in a gene activity matrix). This step introduces an important level of approximation, which could affect the analysis later performed. Here we propose MOWGAN (Multi Omic Wasserstein Generative Adversarial Network), a deep-learning based framework to simulate paired multimodal data supporting high number of modalities (more than two) and agnostic about their relationships (no assumption is imposed). Each modality is embedded into feature spaces with same dimensionality across all modalities. This step prevents any conversion between data modalities. The embeddings are sorted based on the first Laplacian Eigenmap. Mini-batches are selected by a Bayesian ridge regressor to train a Wasserstein Generative Adversarial Network with gradient penalty. The output of the generative network is used to bridge real unpaired data. MOWGAN was prototyped on public data for which paired and unpaired RNA and ATAC experiments exists. Evaluation was conducted on the ability to produce data integrable with the original ones, on the amount of shared information between synthetic layers and on the ability to impose association between molecular layers that are truly connected. The organization of the embeddings in mini-batches allows MOWGAN to have a network architecture independent of the number of modalities evaluated. Indeed, the framework was also successfully applied to integrate three (e.g., RNA, ATAC and protein or histone modification data) and four modalities (e.g., RNA, ATAC, protein, histone modifications). MOWGAN’s performance was evaluated in terms of both computational scalability and biological meaning, being the latter the most important to avoid erroneous conclusion. A comparison was conducted with published methods, concluding that MOWGAN performs better when looking at the ability to retrieve the correct biological identity (e.g., cell types) and associations. In conclusion, MOWGAN is a powerful tool for multi-omics data integration in single-cell, which answer most of the critical issues observed in the field.
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Book chapters on the topic "WGAN-GP"

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Li, Jun, Ke Niu, Liwei Liao, Lijie Wang, Jia Liu, Yu Lei, and Minqing Zhang. "A Generative Steganography Method Based on WGAN-GP." In Communications in Computer and Information Science, 386–97. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8083-3_34.

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Huang, Wei, Mingyuan Luo, Xi Liu, Peng Zhang, Huijun Ding, and Dong Ni. "Arterial Spin Labeling Images Synthesis via Locally-Constrained WGAN-GP Ensemble." In Lecture Notes in Computer Science, 768–76. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32251-9_84.

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Hu, Jiayuan, and Yong Li. "Electrocardiograph Based Emotion Recognition via WGAN-GP Data Enhancement and Improved CNN." In Intelligent Robotics and Applications, 155–64. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13844-7_16.

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Zhu, Shaojun, and Fei Han. "A Data Enhancement Method for Gene Expression Profile Based on Improved WGAN-GP." In Neural Computing for Advanced Applications, 242–54. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5188-5_18.

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Huang, Wei, Mingyuan Luo, Xi Liu, Peng Zhang, Huijun Ding, and Dong Ni. "Novel Bi-directional Images Synthesis Based on WGAN-GP with GMM-Based Noise Generation." In Machine Learning in Medical Imaging, 160–68. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32692-0_19.

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Kavran, Domen, Borut Žalik, and Niko Lukač. "Comparing Beta-VAE to WGAN-GP for Time Series Augmentation to Improve Classification Performance." In Lecture Notes in Computer Science, 51–73. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-22953-4_3.

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Lee, WooHo, BongNam Noh, YeonSu Kim, and KiMoon Jeong. "Generation of Network Traffic Using WGAN-GP and a DFT Filter for Resolving Data Imbalance." In Internet and Distributed Computing Systems, 306–17. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34914-1_29.

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Conference papers on the topic "WGAN-GP"

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Fu, Weiwei, Dezheng Zhang, Yu Fu, Jianyuan Li, and Yonghong Xie. "Arrears prediction for electricity customer through Wgan-Gp." In 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2017. http://dx.doi.org/10.1109/itnec.2017.8285078.

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Wu, Jianyuan, Zheng Wang, Hui Zeng, and Xiangui Kang. "Multiple-Operation Image Anti-Forensics with WGAN-GP Framework." In 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2019. http://dx.doi.org/10.1109/apsipaasc47483.2019.9023173.

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Li, Yiteng, Xupeng He, Weiwei Zhu, Marwa AlSinan, Hyung Kwak, and Hussein Hoteit. "Digital Rock Reconstruction Using Wasserstein GANs with Gradient Penalty." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-21884-ms.

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Abstract Due to the scarcity and vulnerability of physical rock samples, digital rock reconstruction plays an important role in the numerical study of reservoir rock properties and fluid flow behaviors. With the rapid development of deep learning technologies, generative adversarial networks (GANs) have become a promising alternative to reconstruct complex pore structures. Numerous GAN models have been applied in this field, but many of them suffer the unstable training issue. In this study, we apply the Wasserstin GAN with gradient penalty, also known as the WGAN-GP network, to reconstruct Berea sandstone and Ketton limestone. Unlike many other GANs using the Jesnen-Shannon divergence, the WGAN-GP network exhibits a stable training performance by using the Wasserstin distance to measure the difference between generated and real data distributions. Moreover, the generated image quality correlates with the discriminator loss. This provides us an indicator of the training state instead of frequently subjective assessments in the training of deep convolutional GAN (DCGAN) based models. An integrated framework is presented to automate the entire workflow, including training set generation, network setup, model training and synthetic rock validation. Numerical results show that the WGAN-GP network accurately reconstructs both Berea sandstone and Ketton limestone in terms of two-point correlation and morphological properties.
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Moghadam, Atefeh Ziaei, Hamed Azarnoush, and Seyyed Ali Seyyedsalehi. "Multi WGAN-GP loss for pathological stain transformation using GAN." In 2021 29th Iranian Conference on Electrical Engineering (ICEE). IEEE, 2021. http://dx.doi.org/10.1109/icee52715.2021.9544310.

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Moghadam, Atefeh Ziaei, Hamed Azarnoush, and Seyyed Ali Seyyedsalehi. "Multi WGAN-GP loss for pathological stain transformation using GAN." In 2021 29th Iranian Conference on Electrical Engineering (ICEE). IEEE, 2021. http://dx.doi.org/10.1109/icee52715.2021.9544310.

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Ge, Na, Wenhui Guo, and Yanjiang Wang. "Globally Consistent Image Inpainting based on WGAN-GP Network optimization." In 2022 16th IEEE International Conference on Signal Processing (ICSP). IEEE, 2022. http://dx.doi.org/10.1109/icsp56322.2022.9965358.

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Zhao, Kainan, Baoliang Dong, and Cheng Yang. "Military Target Recognition Technology based on WGAN-GP and XGBoost." In CSSE 2021: 2021 4th International Conference on Computer Science and Software Engineering. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3494885.3494925.

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wu, cheng-hsuan, Hsien-Tsung Chang, and Ammar Amjad. "Eye in-painting using WGAN-GP for face images with mosaic." In Third International Conference on Image, Video Processing and Artificial Intelligence, edited by Ruidan Su. SPIE, 2020. http://dx.doi.org/10.1117/12.2580635.

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Wang, Jing-Tong, and Chih-Hung Wang. "High Performance WGAN-GP based Multiple-category Network Anomaly Classification System." In 2019 International Conference on Cyber Security for Emerging Technologies (CSET). IEEE, 2019. http://dx.doi.org/10.1109/cset.2019.8904890.

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Qu, Lele, Yutong Wang, Tianhong Yang, Lili Zhang, and Yanpeng Sun. "WGAN-GP-Based Synthetic Radar Spectrogram Augmentation in Human Activity Recognition." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9554556.

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