Synthesis and evaluation of seamless, large-scale, multispectral satellite images using Generative Adversarial Networks on land use and land cover and Sentinel-2 data by Torben Dedring, Andreas Rienow

“…We train two identical Conditional Generative Adversarial Networks (CGAN) to synthesize a multispectral Sentinel-2 image based on different combinations of open-source LULC data sets. …”
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Generative-assisted multi-stage integrated network: Tackling extreme noise in image denoisingThe source code and trained models are made publicly available at: by Taeyong Park, Muhammad Sohail Ibrahim, Minseok Kim, Zahyun Ku, Yunsang Kwak

“…The proposed GainNet integrates three key components: the noise extractor block for iterative noise suppression, the image-to-image translator block leveraging conditional generative adversarial networks for direct noisy-to-clean image translation, and the depth-fusion enhancer block, utilizing a swin-convolution architecture to fuse and refine multi-channel inputs. …”
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Intelligent Diagnosis Method of Gear Faults based on MGAN and CNN by Zheng Li, Shengli Wu, Wenting Xing, Shiyong Liao

“…An intelligent diagnosis method of gear faults based on MGAN （Mixture Generative Adversarial Nets） and CNN （Convolutional Neural Networks） is proposed. …”
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Artificial intelligence applications in pediatric ophthalmology: A comprehensive review by Anupama Janardhanan, Meenakshi Ravindran, Allapitchai Fathima, Neelam Pawar

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Generating Synthetic Datasets with Deep Learning Models for Human Physical Fatigue Analysis by Arsalan Lambay, Ying Liu, Phillip Morgan, Ze Ji

“…This study presents an innovative approach utilising conditional GAN with auxiliary conditioning to generate synthetic datasets with essential features for detecting human physical fatigue in industrial scenarios. …”
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Multi-Scale Feature Fusion GANomaly with Dilated Neighborhood Attention for Oil and Gas Pipeline Sound Anomaly Detection by Yizhuo Zhang, Zhengfeng Sun, Shen Shi, Huiling Yu

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SAR-PATT: A Physical Adversarial Attack for SAR Image Automatic Target Recognition by Binyan Luo, Hang Cao, Jiahao Cui, Xun Lv, Jinqiang He, Haifeng Li, Chengli Peng

“…Inspired by optical images, current SAR ATR adversarial example generation is performed in the image domain. …”
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Fault Diagnosis of Magnetically Controlled On-Column Circuit Breaker Based on Small Sample Condition by He Tian, Chao Liang, Wenpeng Ma, Tianchang Zhang

“…Initially, a Variational Autoencoder (VAE) is employed to extract the latent distribution of genuine samples, which are then integrated with the Auxiliary Classifier Generative Adversarial Network (ACGAN) generator to learn the characteristics of real data. …”
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Pose measurement method for coal mine drilling robot based on deep learning by Jiangnan LUO, Jianping LI, Hongxiang JIANG, Deyi ZHANG

“…Next, a generative adversarial network was integrated into the PointNet++ to capture more complex and detailed point cloud features. …”
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A systematic review of deep learning chemical language models in recent era by Hector Flores-Hernandez, Emmanuel Martinez-Ledesma

“…Transformers, recurrent neural networks (RNNs), generative adversarial networks (GANs), Structured Space State Sequence (S4) models, and variational autoencoders (VAEs) are considered the main deep learning architectures used for molecule generation in the set of retrieved articles. …”
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An optimized domain-specific shrimp detection architecture integrating conditional GAN and weighted ensemble learning by L. Ravi Kumar, Ravi Kumar Tata, T. R. Mahesh, Endris Mohammed Ali

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Analysis and Evolution Trend of Temperature-Sensitive Loads for Virtual Power Plant Operation by Ying ZHOU, Xuefeng BAI, Yang WANG, Min QIU, Chong SUN, Yajie WU, Bin LI

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Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support by Mahmud Omar, Vera Sorin, Jeremy D. Collins, David Reich, Robert Freeman, Nicholas Gavin, Alexander Charney, Lisa Stump, Nicola Luigi Bragazzi, Girish N. Nadkarni, Eyal Klang

“…We tested six LLMs under three conditions: default (standard settings), mitigating prompt (designed to reduce hallucinations), and temperature 0 (deterministic output with maximum response certainty), generating 5,400 outputs. …”
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Leveraging BiLSTM-CRF and adversarial training for sentiment analysis in nature-based digital interventions: Enhancing mental well-being through MOOC platforms by Juanjuan Zang

“…This involves incorporating perturbations in the embedding space, generating adversarial samples at the embedding layer and semantic feature fusion layer, and combining these with the original samples for model training. …”
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One-shot generative distribution matching for augmented RF-based UAV identification by Amir Kazemi, Salar Basiri, Volodymyr Kindratenko, Srinivasa Salapaka

“…This approach, when utilizing a distributional distance metric, demonstrates significant promise in low-data regimes, outperforming deep generative methods such as conditional generative adversarial networks (GANs) and variational autoencoders (VAEs). …”
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A Transfer Learning Method to Generate Synthetic Synoptic Magnetograms by Xiaoyue Li, Valliappan Senthamizh Pavai, Daria Shukhobodskaia, Mark D. Butala, Luciano Rodriguez, Jasmina Magdalenic, Véronique Delouille

“…Toward this goal, we develop a method called Transfer‐Solar‐GAN which combines a conditional generative adversarial network with a transfer learning approach to overcome training data set limitations. …”
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Unknown intrusion traffic detection method based on unsupervised learning and open-set recognition by Jun Fang, Cunxiang Xie

“…To address problems relating to the low classification accuracy of current intrusion traffic detection algorithms and that most of the current research focus on closed set detection, this paper proposes a detection and classification model for open set traffic based on information maximization generative adversarial network and OpenMax algorithm. …”
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Multi-Channel Speech Enhancement Using Labelled Random Finite Sets and a Neural Beamformer in Cocktail Party Scenario by Jayanta Datta, Ali Dehghan Firoozabadi, David Zabala-Blanco, Francisco R. Castillo-Soria

“…In this research, a multi-channel target speech enhancement scheme is proposed that is based on deep learning (DL) architecture and assisted by multi-source tracking using a labeled random finite set (RFS) framework. A neural network based on minimum variance distortionless response (MVDR) beamformer is considered as the beamformer of choice, where a residual dense convolutional graph-U-Net is applied in a generative adversarial network (GAN) setting to model the beamformer for target speech enhancement under reverberant conditions involving multiple moving speech sources. …”
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Real-time earthquake magnitude prediction using designed machine learning ensemble trained on real and CTGAN generated synthetic data by Anushka Joshi, Balasubramanian Raman, C. Krishna Mohan

“…This imbalance causes a high prediction error while training advanced machine learning or deep learning models. In this work, Conditional Tabular Generative Adversarial Networks (CTGAN), a deep machine learning tool, is utilized to learn the characteristics of the first arrival of earthquake P-waves and generate a synthetic dataset based on this information. …”
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Neutrosophic Intelligence for Secure UAV Communication: A Machine Learning Framework for Uncertainty-Aware Link Classification by Muhammad Edmerdash, Waleed khedr, Ehab Rushdy

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