Modeling of microstructure evolution during high-temperature oxidation of porous Fe-Cr steels by Samih Haj Ibrahim, Damian Koszelow, Małgorzata Makowska, Sebastian Molin

“…Herein, porous microstructures of the porous Fe-Cr steels obtained from SEM imaging, X-ray tomography, and artificial 3D models generated with the use of Generative Adversarial Networks were used as test cases. The obtained results demonstrated high compliance with the experimental evaluation of porosity evolution and chromium content decrease during oxidation at 700 °C for 3000 h. …”
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Music Generation Using Deep Learning and Generative AI: A Systematic Review by Rohan Mitra, Imran Zualkernan

“…The study examines common data representations in music generation, including raw waveforms, spectrograms, and MIDI, alongside the most prominent deep learning architectures like Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Variational Autoencoders (VAEs), and Transformer-based models. …”
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Effectiveness of the Spatial Domain Techniques in Digital Image Steganography by Rosshini Selvamani, Yusliza Yusoff

“…In addition to using statistics as a foundation, convolution neural networks (CNN), generative adversarial networks (GAN), coverless approaches, and machine learning are all used to construct steganographic methods. …”
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Application of Rotating Machinery Fault Diagnosis Based on Deep Learning by Wei Cui, Guoying Meng, Aiming Wang, Xinge Zhang, Jun Ding

“…After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault diagnosis of rotating machinery. …”
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Classroom Expression Recognition Based on Deep Learning by Yang Gao, Linyan Zhou, Jialiang He

“…To address the problem of insufficient data volume, this paper introduces a data enhancement method based on generative adversarial networks to improve the diversity and quality of the dataset. …”
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Deep Learning Approaches for 3D Model Generation from 2D Artworks to Aid Blind People with Tactile Exploration by Rocco Furferi

“…The survey explores the potentiality of Convolutional Neural Networks, Generative Adversarial Networks, Variational Autoencoders, and zero-shot methods. …”
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GAN-based pseudo random number generation optimized through genetic algorithms by Xuguang Wu, Yiliang Han, Minqing Zhang, Yu Li, Su Cui

“…In this paper, we present a Genetic Algorithm Optimized Generative Adversarial Network (hereinafter referred to as GAGAN), which is designed for the effective training of discrete generative adversarial networks. …”
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TEC Map Completion Through a Deep Learning Model: SNP‐GAN by Yang Pan, Mingwu Jin, Shunrong Zhang, Yue Deng

“…Compared to the conventional image inpainting methods, the deep learning methods using generative adversarial networks (GANs) offer an effective image inpainting tool. …”
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The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection by Tarek Berghout

“…Other methods, such as Generative Adversarial Networks (GANs) and Autoencoders, are used for feature extraction, while Recurrent Neural Networks (RNNs) are employed for time-sequence modeling. …”
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Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities by Izabela Rojek, Dariusz Mikołajewski, Krzysztof Galas, Adrianna Piszcz

“…Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) facilitate detailed analysis of spatial and temporal data to better predict energy usage. Generative adversarial networks (GANs) are used to simulate energy usage scenarios, supporting strategic planning and anomaly detection. …”
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A Deep Transfer Learning Method for Bearing Fault Diagnosis Based on Domain Separation and Adversarial Learning by Shoubing Xiang, Jiangquan Zhang, Hongli Gao, Dalei Shi, Liang Chen

“…In DSRAN, domain-difference and domain-invariant feature extractors are used to extract and separate domain-difference and domain-invariant features, respectively Moreover, the idea of generative adversarial networks (GAN) was used to improve the network in learning domain-invariant features. …”
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Cloud-Integrated Meteorological Parameter Prediction by Leveraging Multivariate Statistical Time Series and GANs by Archana Rout, Biswa Ranjan Senapati, Debahuti Mishra

“…To address data gaps, generative adversarial networks (GANs) are employed for data imputation within those parameters. …”
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Beware of diffusion models for synthesizing medical images—a comparison with GANs in terms of memorizing brain MRI and chest x-ray images by Muhammad Usman Akbar, Wuhao Wang, Anders Eklund

“…Preceded by generative adversarial networks (GANs), diffusion models have shown impressive results using various evaluation metrics. …”
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Novel GSIP: GAN-based sperm-inspired pixel imputation for robust energy image reconstruction by Gamal M. Mahmoud, Wael Said, Magdy M. Fadel, Mostafa Elbaz

“…This paper introduces a novel approach for missing pixel imputation based on Generative Adversarial Networks (GANs). We propose a new GAN architecture incorporating an identity module and a sperm motility-inspired heuristic during filtration to optimize the selection of pixels used in reconstructing missing data. …”
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Improved Localization and Recognition of Handwritten Digits on MNIST Dataset with ConvGRU by Yalin Wen, Wei Ke, Hao Sheng

“…Firstly, we introduce a specialized decoupling model using modified Generative Adversarial Networks (GANs) that effectively separates background and foreground information, significantly improving prediction accuracy. …”
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Adaptive Learning Algorithms for Low Dose Optimization in Coronary Arteries Angiography: A Comprehensive Review by Komal Tariq, Muhammad Adnan Munir, Hafiza Tooba Aftab, Amir Naveed, Ayesha Yousaf, Sajjad Ul Hassan

“…Innovative methods such as Model-Based Deep Learning (MBDL) and Self-Attention Generative Adversarial Networks (SAGAN) demonstrate efficient reconstruction capabilities. …”
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Vulnerability analysis on random matrix theory for power grid with flexible impact loads by Chuan Long, Shengyong Ye, Xinying Zhu, Minghai Xu, Xinting Yang, Yuqi Han, Liyang Liu

“…In this paper, we first constructed a rail transit load model based on Deep Convolutional Generative Adversarial Networks (DCGAN) to simulate the situation that massive rail transit load merged into the Grid Scenario. …”
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Defend Against Property Inference Attack for Flight Operations Data Sharing in FedMeta Framework by Jin Lei, Weiyun Li, Meng Yue, Zhijun Wu

“…Aiming at property inference attacks in shared application model training, we proposed FedMeta-CTGAN, a novel approach that leverages federated meta-learning and conditional tabular generative adversarial networks (CTGANs) to protect flight operations data. …”
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Deep Learning for Traffic Scene Understanding: A Review by Parya Dolatyabi, Jacob Regan, Mahdi Khodayar

“…The paper synthesizes insights from a broad range of studies, tracing the evolution from traditional image processing methods to sophisticated DL techniques, such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). The review also explores three primary categories of domain adaptation (DA) methods: clustering-based, discrepancy-based, and adversarial-based, highlighting their significance in traffic scene understanding. …”
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Detecting Subtle Cyberattacks on Adaptive Cruise Control Vehicles: A Machine Learning Approach by Tianyi Li, Mingfeng Shang, Shian Wang, Raphael Stern

“…We introduce a new anomaly detection model based on generative adversarial networks (GAN) designed for the real-time pinpointing of such attacks using vehicle trajectory data. …”
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