Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs by Jing Li, Wei Zong, Yang-Wai Chow, Willy Susilo

“…These results demonstrate the effectiveness and practicality of SNNL as a general enhancement for GAN-based data augmentation in imbalanced NIDS tasks.…”
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Augmentation method of fatigue data of welded structures based on physics-informed CTGAN by Xinyu Cao, Li Zou, Chen Lu

“…The machine learning models' accuracy and generalization capabilities are impacted by this. This work introduces a novel data augmentation approach utilizing physics-informed Generative Adversarial Networks (GAN). …”
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The research on enhancing LA estimation accuracy across domains for small sample data based on data augmentation and data transfer integration optimization system by Ai-Dong Wang, Rui-Jie Li, Xiang-Qian Feng, Zi-Qiu Li, Wei-Yuan Hong, Hua-Xing Wu, Dan-Ying Wang, Song Chen

“…A comprehensive comparison of six algorithms (linear regression, support vector regression, random forest, XGBoost, CatBoost, and K-nearest neighbors) is conducted, assessing their performance under a combined strategy of data augmentation (noise injection, generative adversarial networks, Gaussian mixture model, variational autoencoders) and transfer learning (random, clustering, and hierarchical parameter transfer). …”
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Single-Scene SAR Image Data Augmentation Based on SBR and GAN for Target Recognition by Shangchen Feng, Xikai Fu, Yanlin Feng, Xiaolei Lv

“…By employing simulated SAR images for data augmentation, the accuracy of target recognition networks can be consistently and significantly enhanced.…”
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Data generation for asphalt pavement evaluation: Deep learning-based insights from generative models by Mohammad Sedighian-Fard, Amir Golroo, Mahdi Javanmardi, Alexandre Alahi, Mehdi Rasti

“…Using the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) to generate realistic pavement crack images, the U-Net segmentation model is optimized. …”
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MDFT-GAN: A Multi-Domain Feature Transformer GAN for Bearing Fault Diagnosis Under Limited and Imbalanced Data Conditions by Chenxi Guo, Vyacheslav V. Potekhin, Peng Li, Elena A. Kovalchuk, Jing Lian

“…While generative adversarial networks (GANs) have shown promise in data augmentation, their efficacy deteriorates in the presence of multi-category and structurally complex fault distributions. …”
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A Data Resource Trading Price Prediction Method Based on Improved LightGBM Ensemble Model by Wan Nie, Bingliang Shen, Desheng Li

“…To address the key challenges of limited practical application, high implementation difficulty, and poor generalization capability in existing theoretical models for data resource pricing, this study employs generative adversarial network (GAN) to augment the dataset and constructs a DRV-LightGBM model based on a Bayesian parameter optimization algorithm that maximizes the coefficient of determination (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) to predict data resource transaction prices and provide post-hoc explanations for the prediction model. …”
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Self Attention GAN and SWIN Transformer-Based Pothole Detection With Trust Region-Based LSM and Hough Line Transform for 2D to 3D Conversion by R. Kothai, N. Prabakaran

“…In this paper, we have addressed the aforementioned problem with deep learning based General Adversarial Networks (GAN) along with Data Augmentation considering both road damaged and road damage free images from the datasets. …”
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Efficient dataset extension using generative networks for assessing degree of coating degradation around scribe by Dominik Stursa, Pavel Rozsival, Petr Dolezel

“…Deep convolutional generative adversarial networks (DCGAN) are employed to generate synthetic input-target pairs, which closely resemble real-world data, with the goal of expanding an existing dataset. …”
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Unsupervised Domain Adaptation via Contrastive Learning and Complementary Region-Class Mixing by Xiaojing Li, Wei Zhou, Mingjian Jiang

“…In semantic segmentation, current deep convolutional neural networks rely heavily on extensive data to achieve superior segmentation results. …”
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A novel ensemble Wasserstein GAN framework for effective anomaly detection in industrial internet of things environments by Rubina Riaz, Guangjie Han, Kamran Shaukat, Naimat Ullah Khan, Hongbo Zhu, Lei Wang

“…To address this, we introduce a two-stage generative oversampling framework called Enhanced Optimization of Wasserstein Generative Adversarial Network (EO-WGAN). This enhanced WGAN-based Oversampling approach combines the strengths of the Synthetic Minority Oversampling Technique (SMOTE) and Wasserstein Generative Adversarial Networks (WGAN). …”
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A Data-Driven Approach for Automatic Aircraft Engine Borescope Inspection Defect Detection Using Computer Vision and Deep Learning by Thibaud Schaller, Jun Li, Karl W. Jenkins

“…In addition, synthetic images are generated using Deep Convolutional Generative Adversarial Networks and a manual data augmentation approach by randomly pasting defects onto reactor blade images. …”
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Deep learning-based artificial intelligence for assisting diagnosis, assessment and treatment in soft tissue sarcomas by Ruiling Xu, Jinxin Tang, Chenbei Li, Hua Wang, Lan Li, Yu He, Chao Tu, Zhihong Li

“…Besides, the reinforcement of the model by transfer learning and generative adversarial network (GAN) for data augmentation has also been elaborated. …”
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Fault diagnosis methods for imbalanced samples of hydraulic pumps based on DA-DCGAN by Yang Zhao, Anqi Jiang, Wanlu Jiang, Peiyao Zhang, Peng Xiong

“…A dual attention-deep convolutional generative adversarial network (DA-DCGAN) is proposed to generate fault signals and enhance diagnosis under imbalanced conditions.Initially, fault vibration signals are converted into time-frequency maps using continuous wavelet transform (CWT) to highlight key features. …”
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Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods by Zhishui You, Yuzhu Guo, Xiulei Zhang, Yifan Zhao

“…This paper undertakes a comprehensive and thorough review of the techniques and methodologies underpinning the generative models of the general EEG, namely the variational autoencoder (VAE), the generative adversarial network (GAN), and the diffusion model. …”
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Augmenting Insufficiently Accruing Oncology Clinical Trials Using Generative Models: Validation Study by Samer El Kababji, Nicholas Mitsakakis, Elizabeth Jonker, Ana-Alicia Beltran-Bless, Gregory Pond, Lisa Vandermeer, Dhenuka Radhakrishnan, Lucy Mosquera, Alexander Paterson, Lois Shepherd, Bingshu Chen, William Barlow, Julie Gralow, Marie-France Savard, Christian Fesl, Dominik Hlauschek, Marija Balic, Gabriel Rinnerthaler, Richard Greil, Michael Gnant, Mark Clemons, Khaled El Emam

“…Four different generative models were evaluated: sequential synthesis with decision trees, Bayesian network, generative adversarial network, and a variational autoencoder. …”
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Design of an Improved Method for Task Scheduling Using Proximal Policy Optimization and Graph Neural Networks by Nemilidinea Anantharami Reddy, B.V. Gokulnath

“…We provide an integrated scheduling framework that integrates Proximal Policy Optimization, Graph Neural Networks, hybrid rule-based and machine learning techniques, and synthetic data generation with Generative Adversarial Networks in this paper. …”
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Automated Cardiac Disease Prediction Using Composite GAN and DeepLab Model by Sohail Jabbar, Umar Raza, Muhammad Asif Habib, Muhammad Farhan, Saqib Saeed

“…However, constraints like limited annotation and model generalization persist. We introduce GenDeep, a novel framework integrating an unsupervised Generative Adversarial Network (GAN) and DeepLab model for robust cardiac pathology classification from cine-MRI scans. …”
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A Dual-Encoder Contrastive Learning Model for Knowledge Tracing by Yanhong Bai, Xingjiao Wu, Tingjiang Wei, Liang He

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Cross-dataset evaluation of deep learning models for crack classification in structural surfaces by Rashid Taha, Mokji Musa Mohd, Rasheed Mohammed

“…Potential areas of investigation may be the advanced domain adaptation, generative adversarial network-based data synthesis, and hybrid modeling strategies, which may be utilized to increase the robustness of the model. …”
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