Federated Analytics With Data Augmentation in Domain Generalization Toward Future Networks by Xunzheng Zhang, Juan Marcelo Parra-Ullauri, Shadi Moazzeni, Xenofon Vasilakos, Reza Nejabati, Dimitra Simeonidou

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Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks by Yao-San Lin, Hung-Yu Chen, Mei-Ling Huang, Tsung-Yu Hsieh

“…Voiceprint recognition systems often face challenges related to limited and diverse datasets, which hinder their performance and generalization capabilities. This study proposes a novel approach that integrates generative adversarial networks (GANs) for data augmentation and convolutional neural networks (CNNs) with mel-frequency cepstral coefficients (MFCCs) for voiceprint classification. …”
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Data efficiency assessment of generative adversarial networks in energy applications by Umme Mahbuba Nabila, Linyu Lin, Xingang Zhao, William L. Gurecky, Pradeep Ramuhalli, Majdi I. Radaideh

“…This study investigates the data requirements of generative artificial intelligence (AI), particularly generative adversarial networks (GANs), for reliable data augmentation in energy applications. …”
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AEGAN-Pathifier: a data augmentation method to improve cancer classification for imbalanced gene expression data by Qiaosheng Zhang, Yalong Wei, Jie Hou, Hongpeng Li, Zhaoman Zhong

“…Thus, we incorporate prior knowledge from the pathway and combine AutoEncoder and Generative Adversarial Network (GAN) to solve these difficulties. …”
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Skeleton-Based Data Augmentation for Sign Language Recognition Using Adversarial Learning by Yuriya Nakamura, Lei Jing

“…Therefore, we focus on visual-based SLR using skeletal data and propose an adversarial learning SLR model called Adversarial Vulnerability-Seeking Networks (AVSN), which jointly trains two independent processes, data augmentation, and machine learning. …”
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LarGAN: A Label Auto-Rescaling Generation Adversarial Network for Rare Surface Defects by Guan Qin, Hanxin Zhang, Ke Xu, Liaoting Pan, Lei Huang, Xuezhong Huang, Yi Wei

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Conditional Generative Adversarial Networks and Deep Learning Data Augmentation: A Multi-Perspective Data-Driven Survey Across Multiple Application Fields and Classification Archit... by Lucas C. Ribas, Wallace Casaca, Ricardo T. Fares

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Spectroscopic Method for Detection of Soluble Solid Content in Cherry Tomato Using Deep Convolutional Generative Adversarial Network-Based Data Augmentation by WU Zhijing, LIU Fuqiang, LI Zhigang, CHEN Hui

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An Integrated Algorithm with Feature Selection, Data Augmentation, and XGBoost for Ovarian Cancer by Jingxun Cai, Zne-Jung Lee, Zhihxian Lin, Chih-Hung Hsu, Yun Lin

“…This research offers a prediction model utilizing genomic data to enhance the early diagnosis rate of ovarian cancer, incorporating feature selection, data augmentation through adversarial conditional generative adversarial networks (AC-GAN), and an extreme gradient boosting (XGBoost) classifier. …”
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Desensitized Financial Data Generation Based on Generative Adversarial Network and Differential Privacy by Fan Zhang, Luyao Wang, Xinhong Zhang

“…This paper proposes a Noise Visibility Function-Differential Privacy Generative Adversarial Network (NVF-DPGAN) model, which generates privacy preserving data similar to the original data, and can be applied to data augmentation for deep learning. …”
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Wind Turbine Fault Diagnosis with Imbalanced SCADA Data Using Generative Adversarial Networks by Hong Wang, Taikun Li, Mingyang Xie, Wenfang Tian, Wei Han

“…This article presents an innovative deep learning-based fault diagnosis method to solve the SCADA data imbalance issue. First, a data generation module centered on generative adversarial networks is designed to create a balanced dataset. …”
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Multimodal data fusion for Alzheimer's disease based on dynamic heterogeneous graph convolutional neural network and generative adversarial network by Xiaoyu Chen, Shuaiqun Wang, Wei Kong

“…The complex and diverse causes of AD make it challenging to fully exploit the complementary information among different data types. To address these challenges, we propose a multi-modal data fusion method based on a Dynamic Heterogeneous Attention Network (DHAN) and Generative Adversarial Networks (GAN). …”
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General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning by Yilong Zhang, Tianke Wang, Kang Du, Peng Chen, Haixia Wang, Haohao Sun

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Ultrasonic wave field image augmentation in PZT sensors using generative machine learning and Coulomb coupling by Banerjee Pragyan, Ojha Shivam, Kalimullah Nur M. M., Shelke Amit, Habib Anowarul

“…This paper presents an approach to overcome the time-intensive nature of the Coulomb coupling imaging method by employing Generative Adversarial Networks (GANs) for data augmentation. Coulomb coupling, an experimental technique, is essential for visualizing ultrasonic wave propagation in piezoelectric materials and is valuable in various domains including materials research. …”
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Enhancing generalization in a Kawasaki Disease prediction model using data augmentation: Cross-validation of patients from two major hospitals in Taiwan. by Chuan-Sheng Hung, Chun-Hung Richard Lin, Jain-Shing Liu, Shi-Huang Chen, Tsung-Chi Hung, Chih-Min Tsai

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Coffee plant disease identification with an attentive multi-image segmentation framework (MISF) with CycleGAN by Savitri Kulkarni, P. Deepa Shenoy, K.R. Venugopal

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Counterfactual Examples for Data Augmentation: A Case Study by Md Golam Moula Mehedi Hasan, Douglas A. Talbert

“…We compare our approach with Generative Adversarial Networks approach for dataset augmentation. …”
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Thyroid disease classification using generative adversarial networks and Kolmogorov-Arnold network for three-class classification by Aysel Topşir, Ferdi Güler, Ecesu Çetin, Mehmet Furkan Burak, Melih Ağraz

“…This study introduces an advanced machine learning approach that integrates generative adversarial networks (GANs) for data augmentation and Kolmogorov-Arnold networks (KANs) for classification. …”
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Overall Layout Method of Frame Structure Plane Based on Generative Adversarial Network by ZHONG Yan, LEI Xin, LONG Danbing, FANG Changjian, KANG Yongjun

“…In this paper, addressing the preliminary design phase of architecture and focusing on situations where parts of the structure have already been determined, we propose a framework for the overall layout of the structural plan based on a Generative Adversarial Network (GAN), termed PF‒structGAN. This framework facilitates the design of the structural framework under the dual constraints of both architectural forms and predetermined structural elements. …”
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GaussianMix: Rethinking Receptive Field for Efficient Data Augmentation by A. F. M. Shahab Uddin, Maryam Qamar, Jueun Mun, Yuje Lee, Sung-Ho Bae

“…Mixed Sample Data Augmentation (MSDA) enhances deep learning model generalization by blending a source patch into a target image. …”
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