From Tables to Computer Vision: Transforming HPDC Process Data into Images for CNN-Based Deep Learning by A. Burzyńska

“…The proposed study is founded on two principal pillars: the transformation of process tabular data (generated using the Conditional Tabular Generative Adversarial Network (CTGAN)), involving the mapping of features onto a fixed grid in a heatmap structure, and the configuration of the CNN algorithm to extract complex patterns in the data that are not readily apparent in the original tabular format. …”
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Predicting Post-Liposuction Body Shape Using RGB Image-to-Image Translation by Minji Kim, Jiseong Byeon, Jihun Chang, Sekyoung Youm

“…To achieve this, we utilize data augmentation based on a conditional continuous Generative Adversarial Network (CcGAN), which generates realistic synthetic data conditioned on continuous variables. …”
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Model interpretability on private-safe oriented student dropout prediction. by Helai Liu, Mao Mao, Xia Li, Jia Gao

“…To address these two issues, we introduced for the first time a modified Preprocessed Kernel Inducing Points data distillation technique (PP-KIPDD), specializing in distilling tabular structured dataset, and innovatively employed the PP-KIPDD to reconstruct new samples that serve as qualified training sets simulating student information distributions, thereby preventing student privacy information leakage, which showed better performance and efficiency compared to traditional data synthesis techniques such as the Conditional Generative Adversarial Networks. …”
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Comparative Analysis of Supervised Classification Algorithms for Residential Water End Uses by Zahra Heydari, Ashlynn S. Stillwell

“…In this study, we evaluate the performance of four models (Random Forest, RF; Support Vector Machines, SVM; Logistic Regression, Log‐reg; and Neural Networks, NN) for residential water end‐use classification using actual (measured) and synthetic labeled data sets. We generated synthetic labeled data using Conditional Tabular Generative Adversarial Networks. …”
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TRANSIT your events into a new mass: fast background interpolation for weakly-supervised anomaly searches by I. Oleksiyuk, S. Voloshynovskiy, T. Golling

“…Abstract We introduce a new model for conditional and continuous data morphing called TRansport Adversarial Network for Smooth InTerpolation (TRANSIT). …”
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Diversified Image Inpainting With Transformers and Denoising Iterative Refinement by Shuzhen Xu, Wenlong Xiang, Cuicui Lv, Shuo Wang, Guanhua Liu

“…The diffusion model is stable in training, and the quality of its generated images is already better than that of generative adversarial networks. …”
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Hybrid Deep Learning Methods for Human Activity Recognition and Localization in Outdoor Environments by Yirga Yayeh Munaye, Metadel Addis, Yenework Belayneh, Atinkut Molla, Wasyihun Admass

“…To mitigate data scarcity, this study utilized the conditional tabular generative adversarial network (CTGAN) to generate synthetic channel state information (CSI) data. …”
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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

“…Secondly, we introduce a combined model, the Disease Classifier with CTGAN (CTGAN-DC), which integrates DC with Conditional Tabular Generative Adversarial Network (CTGAN) technology to improve data balance and predictive performance further. …”
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Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things by Jingyao Zhang, Yuan Rao, Chao Man, Zhaohui Jiang, Shaowen Li

“…Subsequently, after implementing rotation and translation, the lesion images were fed into the activation reconstruction generative adversarial networks for data augmentation to generate new training samples. …”
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A GAN-Based Framework with Dynamic Adaptive Attention for Multi-Class Image Segmentation in Autonomous Driving by Bashir Sheikh Abdullahi Jama, Mehmet Hacibeyoglu

“…The most recent deep learning-based image segmentation models have demonstrated impressive performance in structured environments, yet they often fall short when applied to the complex and unpredictable conditions encountered in autonomous driving. This study proposes an Adaptive Ensemble Attention (AEA) mechanism within a Generative Adversarial Network architecture to deal with dynamic and complex driving conditions. …”
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Knowledge-inspired fusion strategies for the inference of PM_2.5 values with a neural network by M. Dabrowski, J. Mennesson, J. Riedi, C. Djeraba, P. Nabat

“…Specifically, novel architectures based on boundary condition generative adversarial networks (BC-GANs, which are able to leverage information from sparse ground observation networks) and on more traditional UNets, employing various information fusion methods, are designed and evaluated against each other. …”
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Research on Super-Resolution Reconstruction of Coarse Aggregate Particle Images for Earth–Rock Dam Construction Based on Real-ESRGAN by Shuangping Li, Lin Gao, Bin Zhang, Zuqiang Liu, Xin Zhang, Linjie Guan, Junxing Zheng

“…The paper begins with a review of traditional image super-resolution methods, introducing Generative Adversarial Networks (GAN) and Real-ESRGAN, which effectively enhance image detail recovery through perceptual loss and adversarial training. …”
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A Survey on Deep Learning for Few-Shot PolSAR Image Classification by Ningwei Wang, Weiqiang Jin, Haixia Bi, Chen Xu, Jinghuai Gao

“…Data augmentation methods enhance the diversity of training samples, with advanced techniques like generative adversarial networks (GANs) generating realistic synthetic data that reflect PolSAR’s polarimetric characteristics. …”
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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

“…Nevertheless, the simulated SAR images generated based on random noise lack constraints, and it is also difficult to generate images that exceed the parameter conditions of the real image’s training set. …”
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Advancing 1.5T MR imaging: toward achieving 3T quality through deep learning super-resolution techniques by Sk Rahatul Jannat, Kirsten Lynch, Maryam Fotouhi, Steve Cen, Steve Cen, Jeiran Choupan, Nasim Sheikh-Bahaei, Nasim Sheikh-Bahaei, Gaurav Pandey, Bino A. Varghese

“…Thus, making it vitally important for diagnosing complex neurological conditions. However, its higher cost of acquisition and operation, increased sensitivity to image distortions, greater noise levels, and limited accessibility in many healthcare settings present notable challenges. …”
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Galaxy Morphology Classification via Deep Semisupervised Learning with Limited Labeled Data by Zhijian Luo, Jianzhen Chen, Zhu Chen, Shaohua Zhang, Liping Fu, Hubing Xiao, Chenggang Shu

“…To address this challenge, we propose an innovative hybrid semisupervised model, Wasserstein GAN for galaxy classification (GC-SWGAN), designed to tackle galaxy morphology classification under conditions of limited labeled data. This model integrates semisupervised generative adversarial networks (GANs) with Wasserstein GAN with gradient penalty, establishing a multitask learning framework. …”
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IonoGAN: An Enhanced Model for Forecasting Quiet and Disturbed Ionospheric Features From Predicted Ionograms by Chu Qiu, Jinhui Cai, Zheng Wang, Pengdong Gao, Guojun Wang, Quan Qi, Bo Wang, Zhengwei Cheng, Jiankui Shi, Yajun Zhu, Xiao Wang, Kai Ding

“…In this paper, IonoGAN, an enhanced neural network based on the Generative Adversarial Network architecture, is proposed for direct prediction of ionograms and the variation of these ionospheric conditions. …”
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Strengthening open disclosure in maternity services in the English NHS: the DISCERN realist evaluation study by Mary Adams, Natalie Sanford, Charlotte Bevan, Maria Booker, Julie Hartley, Alexander Heazell, Elsa Montgomery, Maureen Treadwell, Jane Sandall

“…The challenges of an adversarial medicolegal landscape and limited support for meeting incentivised targets is evidenced. …”
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Cyberattack Monitoring Architectures for Resilient Operation of Connected and Automated Vehicles by Zulqarnain H. Khattak, Brian L. Smith, Michael D. Fontaine

“…Automation requires the use of communication and smart devices, thus introducing potential access points for adversaries. This paper develops a prototype real-time monitoring system for a vehicle to infrastructure (V2I) based CAV system that generates cyberattack data for CAV operations under realistic traffic conditions. …”
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