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Articles de revues sur le sujet « WGAN-GP »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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1

Gadhi, Adel Hassan A., Shelton Peiris, and David E. Allen. "Improving Volatility Forecasting: A Study through Hybrid Deep Learning Methods with WGAN." Journal of Risk and Financial Management 17, no. 9 (2024): 380. http://dx.doi.org/10.3390/jrfm17090380.

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This paper examines the predictive ability of volatility in time series and investigates the effect of tradition learning methods blending with the Wasserstein generative adversarial network with gradient penalty (WGAN-GP). Using Brent crude oil returns price volatility and environmental temperature for the city of Sydney in Australia, we have shown that the corresponding forecasts have improved when combined with WGAN-GP models (i.e., ANN-(WGAN-GP), LSTM-ANN-(WGAN-GP) and BLSTM-ANN (WGAN-GP)). As a result, we conclude that incorporating with WGAN-GP will’ significantly improve the capabilitie

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Xu, Jialing, Jingxing He, Jinqiang Gu, et al. "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

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Yonekura, Kazuo, Yuki Tomori, and Katsuyuki Suzuki. "Airfoil Shape Generation and Feature Extraction Using the Conditional VAE-WGAN-gp." AI 5, no. 4 (2024): 2092–103. http://dx.doi.org/10.3390/ai5040102.

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A machine learning method was applied to solve an inverse airfoil design problem. A conditional VAE-WGAN-gp model, which couples the conditional variational autoencoder (VAE) and Wasserstein generative adversarial network with gradient penalty (WGAN-gp), is proposed for an airfoil generation method, and then, it is compared with the WGAN-gp and VAE models. The VAEGAN model couples the VAE and GAN models, which enables feature extraction in the GAN models. In airfoil generation tasks, to generate airfoil shapes that satisfy lift coefficient requirements, it is known that VAE outperforms WGAN-gp

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Gao, Jiajun. "A comparative study between WGAN-GP and WGAN-CP for image generation." Applied and Computational Engineering 83, no. 1 (2024): 15–19. http://dx.doi.org/10.54254/2755-2721/83/2024glg0059.

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Image generation allows the creation of visual content in a convenient manner. It is critical for enhancing digital experiences, from video games to virtual reality, enabling more engaging and immersive experiences. In current technologies, Generative Adversarial Networks (GANs) have achieved significant success but face challenges like training instability and mode collapse. By utilizing the Wasserstein distance, Wasserstein GAN (WGAN) enhances conventional GANs; however, its weight clipping method may not be ideal. In this study, WGAN with gradient penalty (WGAN-GP) and WGAN with weight clip

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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 (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 res

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Lu, Liyuan. "An Empirical Study of WGAN and WGAN-GP for Enhanced Image Generation." Applied and Computational Engineering 83, no. 1 (2024): 103–9. http://dx.doi.org/10.54254/2755-2721/83/2024glg0066.

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This paper aims to advance the Wasserstein Generative Adversarial Networks (WGANs) and their enhancements, particularly focusing on the gradient penalty. Generative Adversarial Networks (GANs), introduced by Goodfellow et al. in 2014, have revolutionized the domain of image generation. To address the limitations of GANs, the WGAN was proposed. However, WGANs rely on weight clipping, which introduces its own set of issues such as slow convergence and potential gradient vanishing. The inefficiency and instability of WGANs have troubled its users. To solve these problems, WGAN with Gradient Penal

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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 (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 p

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Lee, Gwo-Chuan, Jyun-Hong Li, and Zi-Yang Li. "A Wasserstein Generative Adversarial Network–Gradient Penalty-Based Model with Imbalanced Data Enhancement for Network Intrusion Detection." Applied Sciences 13, no. 14 (2023): 8132. http://dx.doi.org/10.3390/app13148132.

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In today’s network intrusion detection systems (NIDS), certain types of network attack packets are sparse compared to regular network packets, making them challenging to collect, and resulting in significant data imbalances in public NIDS datasets. With respect to attack types with rare data, it is difficult to classify them, even by using various algorithms such as machine learning and deep learning. To address this issue, this study proposes a data augmentation technique based on the WGAN-GP model to enhance the recognition accuracy of sparse attacks in network intrusion detection. The enhan

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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 (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

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Cao, Zhengyu, Wei He, Fengsheng Lin, and Changyi Liu. "A new method for mandala image synthesis based on WGAN-GP." Applied and Computational Engineering 6, no. 1 (2023): 675–81. http://dx.doi.org/10.54254/2755-2721/6/20230895.

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Mandala, as an ancient image art, has been found to have many unexpected applications in recent years, including use in art therapies, meditation induction and human body state assessment. Despite the omnipresent applications of convolutional neural networks in imaging Synthesis, it can be found that there is no work on Mandala Image Synthesis yet. To fill this research gap, deep learning algorithms were considered in this study. With existing research on Generative Adversarial Network (GAN), a typical GAN network called WGAN-GP was used to produce Mandala images. The generator and discriminat

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Rana Saeed Hamdi. "Machine Learning-Driven Random Number Generation: A Comparative Study of WGAN-GP and RNNs for Cryptographic Security." Journal of Information Systems Engineering and Management 10, no. 36s (2025): 792–801. https://doi.org/10.52783/jisem.v10i36s.6564.

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Numerous applications require a high level of randomness, making random number generation an essential component of modern cryptographic systems. Using recurrent neural networks (RNNs) and Generative Adversarial Networks by Wasserstein with Gradient Penalty (WGAN-GP), this paper explores high-quality random number generation using machine learning methods with different complexities. The random sequences produced by the models under consideration could be used in secure cryptographic applications. In addition, while an RNN model captures temporal dependencies to convey complex sequences, a WGA

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Wang, Yifan, Chuan Zhou, Lei Ying, et al. "Enhancing Early Lung Cancer Diagnosis: Predicting Lung Nodule Progression in Follow-Up Low-Dose CT Scan with Deep Generative Model." Cancers 16, no. 12 (2024): 2229. http://dx.doi.org/10.3390/cancers16122229.

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Early diagnosis of lung cancer can significantly improve patient outcomes. We developed a Growth Predictive model based on the Wasserstein Generative Adversarial Network framework (GP-WGAN) to predict the nodule growth patterns in the follow-up LDCT scans. The GP-WGAN was trained with a training set (N = 776) containing 1121 pairs of nodule images with about 1-year intervals and deployed to an independent test set of 450 nodules on baseline LDCT scans to predict nodule images (GP-nodules) in their 1-year follow-up scans. The 450 GP-nodules were finally classified as malignant or benign by a lu

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Hejazi, Shahd, Michael Packianather, and Ying Liu. "A Novel approach using WGAN-GP and Conditional WGAN-GP for Generating Artificial Thermal Images of Induction Motor Faults." Procedia Computer Science 225 (2023): 3681–91. http://dx.doi.org/10.1016/j.procs.2023.10.363.

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Liu, Tong, Xudong Cui, and Li Mo. "A Daily Runoff Prediction Model for the Yangtze River Basin Based on an Improved Generative Adversarial Network." Sustainability 17, no. 7 (2025): 2990. https://doi.org/10.3390/su17072990.

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Hydrological runoff prediction plays a crucial role in water resource management and sustainable development. However, it is often constrained by the nonlinearity, strong stochasticity, and high non-stationarity of hydrological data, as well as the limited accuracy of traditional forecasting methods. Although Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) have been widely used for data augmentation to enhance predictive model training, their direct application as forecasting models remains limited. Additionally, the architectures of the generator and discriminator

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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 Lipsch

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Adiputra, I. Nyoman Mahayasa, Pei-Chun Lin, and Paweena Wanchai. "The Effectiveness of Generative Adversarial Network-Based Oversampling Methods for Imbalanced Multi-Class Credit Score Classification." Electronics 14, no. 4 (2025): 697. https://doi.org/10.3390/electronics14040697.

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Credit score models are essential tools for evaluating creditworthiness and mitigating financial risks. However, the imbalanced nature of multi-class credit score datasets poses significant challenges for traditional classification algorithms, leading to poor performance in minority classes. This study explores the effectiveness of Generative Adversarial Network (GAN)-based oversampling methods, including CTGAN, CopulaGAN, WGAN-GP, and DraGAN, in addressing this issue. By synthesizing realistic data for minority classes and integrating it with majority class data, the study benchmarks these GA

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Kaneva, Tsvetelina, Irena Valova, Katerina Gabrovska-Evstatieva, and Boris Evstatiev. "A Data-Driven Approach for Generating Synthetic Load Profiles with GANs." Applied Sciences 15, no. 14 (2025): 7835. https://doi.org/10.3390/app15147835.

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The generation of realistic electrical load profiles is essential for advancing smart grid analytics, demand forecasting, and privacy-preserving data sharing. Traditional approaches often rely on large, high-resolution datasets and complex recurrent neural architectures, which can be unstable or ineffective when training data are limited. This paper proposes a data-driven framework based on a lightweight 1D Convolutional Wasserstein GAN with Gradient Penalty (Conv1D-WGAN-GP) for generating high-fidelity synthetic 24 h load profiles. The model is specifically designed to operate on small- to me

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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 (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 fo

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Venugopal, Archana, and Diego Resende Faria. "Boosting EEG and ECG Classification with Synthetic Biophysical Data Generated via Generative Adversarial Networks." Applied Sciences 14, no. 23 (2024): 10818. http://dx.doi.org/10.3390/app142310818.

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This study presents a novel approach using Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to generate synthetic electroencephalography (EEG) and electrocardiogram (ECG) waveforms. The synthetic EEG data represent concentration and relaxation mental states, while the synthetic ECG data correspond to normal and abnormal states. By addressing the challenges of limited biophysical data, including privacy concerns and restricted volunteer availability, our model generates realistic synthetic waveforms learned from real data. Combining real and synthetic datasets improve

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Lee, Chanho, Hyukjin Kwon, Hanseon Choi, et al. "A Study on Enhancing the Visual Fidelity of Aviation Simulators Using WGAN-GP for Remote Sensing Image Color Correction." Applied Sciences 14, no. 20 (2024): 9227. http://dx.doi.org/10.3390/app14209227.

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When implementing outside-the-window (OTW) visuals in aviation tactical simulators, maintaining terrain image color consistency is critical for enhancing pilot immersion and focus. However, due to various environmental factors, inconsistent image colors in terrain can cause visual confusion and diminish realism. To address these issues, a color correction technique based on a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) is proposed. The proposed WGAN-GP model utilizes multi-scale feature extraction and Wasserstein distance to effectively measure and adjust the col

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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 (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 c

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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 (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 wi

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Lu, Yang, Xianpeng Tao, Nianyin Zeng, Jiaojiao Du, and Rou Shang. "Enhanced CNN Classification Capability for Small Rice Disease Datasets Using Progressive WGAN-GP: Algorithms and Applications." Remote Sensing 15, no. 7 (2023): 1789. http://dx.doi.org/10.3390/rs15071789.

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An enhancement generator model with a progressive Wasserstein generative adversarial network and gradient penalized (PWGAN-GP) is proposed to solve the problem of low recognition accuracy caused by the lack of rice disease image samples in training CNNs. First, the generator model uses the progressive training method to improve the resolution of the generated samples step by step to reduce the difficulty of training. Second, to measure the similarity distance accurately between samples, a loss function is added to the discriminator that makes the generated samples more stable and realistic. Fi

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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 (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

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Abdulraheem, Abdulkabir, Jamiu T. Suleiman, and Im Y. Jung. "Generative Adversarial Network Models for Augmenting Digit and Character Datasets Embedded in Standard Markings on Ship Bodies." Electronics 12, no. 17 (2023): 3668. http://dx.doi.org/10.3390/electronics12173668.

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Accurate recognition of characters imprinted on ship bodies is essential for ensuring operational efficiency, safety, and security in the maritime industry. However, the limited availability of datasets of specialized digits and characters poses a challenge. To overcome this challenge, we propose a generative adversarial network (GAN) model for augmenting the limited dataset of special digits and characters in ship markings. We evaluated the performance of various GAN models, and the Wasserstein GAN with Gradient Penalty (WGAN-GP) and Wasserstein GAN with divergence (WGANDIV) models demonstrat

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He, Yawen, Nan Xu, Li Cheng, and Haiwen Yuan. "Attention-Guided Wireless Channel Modeling and Generating." Applied Sciences 15, no. 6 (2025): 3058. https://doi.org/10.3390/app15063058.

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Due to the fast advancement in wireless communication technology, the demand for the modeling and generating of wireless channels is increasing. Deep learning technology is gradually applied in the wireless communication field, and the Generative Adversarial Network (GAN) framework provides a new solution for channel modeling. This paper presents a method based on Wasserstein GAN with gradient penalty (WGAN-GP) guided by an attention mechanism for wireless channel modeling and generating. The feature extraction capability of the model is enhanced by adding a channel attention mechanism in WGAN

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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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Du, Zhenlong, Chao Ye, Yujia Yan, and Xiaoli Li. "Low-Dose CT Image Denoising Based on Improved WGAN-gp." Journal of New Media 1, no. 2 (2019): 75–85. http://dx.doi.org/10.32604/jnm.2019.06259.

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KATO, Nobuhito, Keisuke SUZUKI, Yoshihisa KONDO, Katsuyuki SUZUKI, and Kazuo YONEKURA. "Shape Generation of IPM Motor Rotor Using Conditional WGAN-gp." Proceedings of Design & Systems Conference 2023.33 (2023): 3206. http://dx.doi.org/10.1299/jsmedsd.2023.33.3206.

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Berezsky, Oleh M., and Petro B. Liashchynskyi. "Comparison of generative adversarial networks architectures for biomedical images synthesis." Applied Aspects of Information Technology 4, no. 3 (2021): 250–60. http://dx.doi.org/10.15276/aait.03.2021.4.

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The article analyzes and compares the architectures of generativeadversarialnetworks. These networks are based on convolu-tional neural networks that are widely used for classification problems. Convolutional networks require a lot of training data to achieve the desired accuracy. Generativeadversarialnetworks are used for the synthesis of biomedical images in this work. Biomedi-cal images are widely used in medicine, especially in oncology. For diagnosis in oncology biomedical images are divided into three classes: cytological, histological, and immunohistochemical. Initial samples of biomedi

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Abdulraheem, Abdulkabir, and Im Y. Jung. "A Comparative Study of Engraved-Digit Data Augmentation by Generative Adversarial Networks." Sustainability 14, no. 19 (2022): 12479. http://dx.doi.org/10.3390/su141912479.

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In cases where an efficient information retrieval (IR) system retrieves information from images with engraved digits, as found on medicines, creams, ointments, and gels in squeeze tubes, the system needs to be trained on a large dataset. One of the system applications is to automatically retrieve the expiry date to ascertain the efficacy of the medicine. For expiry dates expressed in engraved digits, it is difficult to collect the digit images. In our study, we evaluated the augmentation performance for a limited, engraved-digit dataset using various generative adversarial networks (GANs). Our

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Han, Feng, Xiaojuan Ma, and Jiheng Zhang. "Simulating Multi-Asset Classes Prices Using Wasserstein Generative Adversarial Network: A Study of Stocks, Futures and Cryptocurrency." Journal of Risk and Financial Management 15, no. 1 (2022): 26. http://dx.doi.org/10.3390/jrfm15010026.

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Financial data are expensive and highly sensitive with limited access. We aim to generate abundant datasets given the original prices while preserving the original statistical features. We introduce the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) into the field of the stock market, futures market and cryptocurrency market. We train our model on various datasets, including the Hong Kong stock market, Hang Seng Index Composite stocks, precious metal futures contracts listed on the Chicago Mercantile Exchange and Japan Exchange Group, and cryptocurrency spots and pe

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Zhu, Xining, Lin Zhang, Lijun Zhang, Xiao Liu, Ying Shen, and Shengjie Zhao. "GAN-Based Image Super-Resolution with a Novel Quality Loss." Mathematical Problems in Engineering 2020 (February 18, 2020): 1–12. http://dx.doi.org/10.1155/2020/5217429.

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Single image super-resolution (SISR) has been a very attractive research topic in recent years. Breakthroughs in SISR have been achieved due to deep learning and generative adversarial networks (GANs). However, the generated image still suffers from undesired artifacts. In this paper, we propose a new method named GMGAN for SISR tasks. In this method, to generate images more in line with human vision system (HVS), we design a quality loss by integrating an image quality assessment (IQA) metric named gradient magnitude similarity deviation (GMSD). To our knowledge, it is the first time to truly

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Dai, Yun, Angpeng Liu, Meng Chen, Yi Liu, and Yuan Yao. "Enhanced Soft Sensor with Qualified Augmented Samples for Quality Prediction of the Polyethylene Process." Polymers 14, no. 21 (2022): 4769. http://dx.doi.org/10.3390/polym14214769.

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Data-driven soft sensors have increasingly been applied for the quality measurement of industrial polymerization processes in recent years. However, owing to the costly assay process, the limited labeled data available still pose significant obstacles to the construction of accurate models. In this study, a novel soft sensor named the selective Wasserstein generative adversarial network, with gradient penalty-based support vector regression (SWGAN-SVR), is proposed to enhance quality prediction with limited training samples. Specifically, the Wasserstein generative adversarial network with gra

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Wei, Wutong. "Targeted generative adversarial network (TWGAN-GP)-based emotion recognition of ECG signals." E3S Web of Conferences 522 (2024): 01042. http://dx.doi.org/10.1051/e3sconf/202452201042.

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Emotion is a generic term for a set of subjective cognitive experiences, a mental state and a physiological state resulting from a combination of multiple sensations, thoughts and behaviours. Emotion recognition has a wide range of applications in the medical field, distance education, security and health detection, healthcare, and human-robot interaction. We use ECG signals for emotion recognition, but the difficulties are that it is difficult to obtain high quality physiological signals about emotions and the small sample data make it impossible to train a classifier with high accuracy. To a

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邬, 欣诺. "Optimization and Expansion of Construction Waste Dataset Based on WGAN-GP." Computer Science and Application 13, no. 01 (2023): 136–42. http://dx.doi.org/10.12677/csa.2023.131014.

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Liu, Zhu, Lingfeng Xuan, Dehuang Gong, Xinlin Xie, Zhongwen Liang, and Dongguo Zhou. "A WGAN-GP Approach for Data Imputation in Photovoltaic Power Prediction." Energies 18, no. 5 (2025): 1042. https://doi.org/10.3390/en18051042.

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The increasing adoption of photovoltaic (PV) systems has introduced challenges for grid stability due to the intermittent nature of PV power generation. Accurate forecasting and data quality are critical for effective integration into power grids. However, PV power records often contain missing data due to system downtime, posing difficulties for pattern recognition and model accuracy. To address this, we propose a GAN-based data imputation method tailored for PV power generation. Unlike traditional GANs used in image generation, our method ensures smooth transitions with existing data by util

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Long, Xinyue, and Mingchuan Zhang. "An Overview of Generative Adversarial Networks." Journal of Computing and Electronic Information Management 10, no. 3 (2023): 31–36. http://dx.doi.org/10.54097/jceim.v10i3.8677.

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Generative adversarial network (GAN), put forward by two-person zero-sum game theory, is one of the most important research hotspots in the field of artificial intelligence. With a generator network and a discriminator network, GAN is trained by adversarial learning. In this paper, we aim to discusses the development status of GAN. We first introduce the basic idea and training process of GAN in detail, and summarize the structure and structure of GAN derivative models, including conditional GAN, deep convolution DCGAN, WGAN based on Wasserstein distance and WGAN-GP based on gradient strategy.

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Yuan, Lu, Yuming Ma, and Yihui Liu. "Protein secondary structure prediction based on Wasserstein generative adversarial networks and temporal convolutional networks with convolutional block attention modules." Mathematical Biosciences and Engineering 20, no. 2 (2022): 2203–18. http://dx.doi.org/10.3934/mbe.2023102.

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<abstract> <p>As an important task in bioinformatics, protein secondary structure prediction (PSSP) is not only beneficial to protein function research and tertiary structure prediction, but also to promote the design and development of new drugs. However, current PSSP methods cannot sufficiently extract effective features. In this study, we propose a novel deep learning model WGACSTCN, which combines Wasserstein generative adversarial network with gradient penalty (WGAN-GP), convolutional block attention module (CBAM) and temporal convolutional network (TCN) for 3-state and 8-stat

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Yang, Xiaoli, Lipei Liu, Zhenwei Li, Yuxin Xia, Zhipeng Fan, and Jiayi Zhou. "Semi-Supervised Seizure Prediction Model Combining Generative Adversarial Networks and Long Short-Term Memory Networks." Applied Sciences 13, no. 21 (2023): 11631. http://dx.doi.org/10.3390/app132111631.

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In recent years, significant progress has been made in seizure prediction using machine learning methods. However, fully supervised learning methods often rely on a large amount of labeled data, which can be costly and time-consuming. Unsupervised learning overcomes these drawbacks but can suffer from issues such as unstable training and reduced prediction accuracy. In this paper, we propose a semi-supervised seizure prediction model called WGAN-GP-Bi-LSTM. Specifically, we utilize the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) as the feature learning model, usi

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Shi, Zheng, Yonghao Zhang, Zesheng Hu, et al. "Research on the Enhancement of Provincial AC/DC Ultra-High Voltage Power Grid Security Based on WGAN-GP." Electronics 14, no. 14 (2025): 2897. https://doi.org/10.3390/electronics14142897.

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With the advancement in the “dual carbon” strategy and the integration of high proportions of renewable energy sources, AC/DC ultra-high-power grids are facing new security challenges such as commutation failure and multi-infeed coupling effects. Fault diagnosis, as an important tool for assisting power grid dispatching, is essential for maintaining the grid’s long-term stable operation. Traditional fault diagnosis methods encounter challenges such as limited samples and data quality issues under complex operating conditions. To overcome these problems, this study proposes a fault sample data

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Zhang, Ting, Qingyang Liu, Xianwu Wang, Xin Ji, and Yi Du. "A 3D reconstruction method of porous media based on improved WGAN-GP." Computers & Geosciences 165 (August 2022): 105151. http://dx.doi.org/10.1016/j.cageo.2022.105151.

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Tang, Renhao, Wensi Wang, Qingyu Meng, et al. "A Strabismus Surgery Parameter Design Model with WGAN-GP Data Enhancement Method." Journal of Physics: Conference Series 2179, no. 1 (2022): 012009. http://dx.doi.org/10.1088/1742-6596/2179/1/012009.

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Abstract The purpose of this paper is a machine learning model that could predict the strabismus surgery parameter through the data of patients as accurately as possible. A strabismus surgery parameter design model’s input is a Medical records and return is a surgical value. The Machine learning algorithms is difficult to get a desired result in this process because of the small amount and uneven distribution strabismus surgery data. This paper enhanced the data set through a WGAN-GP model to improve the performance of the LightGBM algorithm. The performance of model is increased from 69.32% t

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Li, Jing, Wei Zong, Yang-Wai Chow, and Willy Susilo. "Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs." Future Internet 17, no. 5 (2025): 216. https://doi.org/10.3390/fi17050216.

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Network Intrusion Detection Systems (NIDS) often suffer from severe class imbalance, where minority attack types are underrepresented, leading to degraded detection performance. To address this challenge, we propose a novel augmentation framework that integrates Soft Nearest Neighbor Loss (SNNL) into Generative Adversarial Networks (GANs), including WGAN, CWGAN, and WGAN-GP. Unlike traditional oversampling methods (e.g., SMOTE, ADASYN), our approach improves feature-space alignment between real and synthetic samples, enhancing classifier generalization on rare classes. Experiments on NSL-KDD,

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Ran, Shuxue. "Applications and challenges of GAN in AI-powered artistry." Applied and Computational Engineering 41, no. 1 (2024): 75–79. http://dx.doi.org/10.54254/2755-2721/41/20230713.

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In the evolving landscape of artificial intelligence (AI), Generative Adversarial Network (GAN), introduced in 2014 by Goodfellow and team, has emerged as a vital pillar in deep learning. Designed around the concept of adversarial learning, GAN consists of a generator and a discriminator working in tandem, with the former creating counterfeit data samples and the latter distinguishing between genuine and counterfeit ones. The paper delves deep into GANs underlying architecture, its modified variants like DCGAN, WGAN, WGAN-GP, and CGAN, and its expansive applications in the realm of AI-powered

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Okada, Kiyoshiro, Katsuhiro Endo, Kenji Yasuoka, and Shuichi Kurabayashi. "Learned pseudo-random number generator: WGAN-GP for generating statistically robust random numbers." PLOS ONE 18, no. 6 (2023): e0287025. http://dx.doi.org/10.1371/journal.pone.0287025.

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Pseudo-random number generators (PRNGs) are software algorithms generating a sequence of numbers approximating the properties of random numbers. They are critical components in many information systems that require unpredictable and nonarbitrary behaviors, such as parameter configuration in machine learning, gaming, cryptography, and simulation. A PRNG is commonly validated through a statistical test suite, such as NIST SP 800-22rev1a (NIST test suite), to evaluate its robustness and the randomness of the numbers. In this paper, we propose a Wasserstein distance-based generative adversarial ne

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Feng, Tianyu, Tao Hu, Wenyu Liu, and Yang Zhang. "Enhancer Recognition: A Transformer Encoder-Based Method with WGAN-GP for Data Augmentation." International Journal of Molecular Sciences 24, no. 24 (2023): 17548. http://dx.doi.org/10.3390/ijms242417548.

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Enhancers are located upstream or downstream of key deoxyribonucleic acid (DNA) sequences in genes and can adjust the transcription activity of neighboring genes. Identifying enhancers and determining their functions are important for understanding gene regulatory networks and expression regulatory mechanisms. However, traditional enhancer recognition relies on manual feature engineering, which is time-consuming and labor-intensive, making it difficult to perform large-scale recognition analysis. In addition, if the original dataset is too small, there is a risk of overfitting. In recent years

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Deng Yuan, 邓源, 施一萍 Shi Yiping, 刘婕 Liu Jie, 江悦莹 Jiang Yueying, 朱亚梅 Zhu Yamei та 刘瑾 Liu Jin. "结合双通道WGAN-GP的多角度人脸表情识别算法研究". Laser & Optoelectronics Progress 59, № 18 (2022): 1810013. http://dx.doi.org/10.3788/lop202259.1810013.

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Jin, Xin, Yuanwen Zou, and Zhongbing Huang. "An Imbalanced Image Classification Method for the Cell Cycle Phase." Information 12, no. 6 (2021): 249. http://dx.doi.org/10.3390/info12060249.

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The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we u

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Ma, Yupu, Ming Ma, Ningbo Wang, and Ying Qiao. "Mid-term Scenario Generation for Wind Power Using GAN with Temporal-correlation Enhancement Block." E3S Web of Conferences 182 (2020): 01003. http://dx.doi.org/10.1051/e3sconf/202018201003.

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Under the background of increasing renewable energy penetration and rigid demand for midterm generation planning, the accurate expression of wind power uncertainty becomes more and more important. Firstly, this paper analyses the problem of scenario generation models based on traditional Generative Adversarial Networks(GAN), point that the fluctuation of scenarios that it generated usually deviates greatly from the real one. And further proposes a convolutional structure, that called Temporal-correlation Enhancement block (TE block), which can solve the aforementioned problem by enhance the te

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