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Integrating GAN-based machine learning with nonlinear Kalman filtering for enhanced state estimation. [PDF]
Sci RepTobaly L, Yaniv E, Zalevsky Z.
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Generative AI in clinical (2020-2025): a mini-review of applications, emerging trends, and clinical challenges. [PDF]
Front Digit HealthFahad N +9 more
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Generative Adversarial Networks in Dermatology: A Narrative Review of Current Applications, Challenges, and Future Perspectives. [PDF]
Bioengineering (Basel)Izu-Belloso RM +2 more
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Generative Adversarial Networks (GANs)
ACM Computing Surveys, 2022Divya Saxena, Jiannong Cao
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Generative Adversarial Networks (GANs)
ACM Computing Surveys, 2021Generative Adversarial Networks (GANs) is a novel class of deep generative models that has recently gained significant attention. GANs learn complex and high-dimensional distributions implicitly over images, audio, and data. However, there exist major challenges in training of GANs, i.e., mode collapse, non-convergence, and instability, due to ...
Saxena, D, Cao, J
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Generative Adversarial Network (GAN) for Simulating Electroencephalography
Brain Topography, 2023Electroencephalographs record the electrical activity of your brain through the scalp. Electroencephalography is difficult to obtain due to its sensitivity and variability. Applications of electroencephalography such as for diagnosis, education, brain-computer interfaces require large samples of electroencephalography recording, however, it is often ...
Priyanshu, Mahey +3 more
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Generative Adversarial Networks (GAN) for Arabic Calligraphy
2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 2021Arabic calligraphy is one of the most aesthetic art forms in the world due to its variety and long history. However, generating calligraphic style is mainly done by human expert calligrapher (also known as Khattat) and has not been carried out by machine learning techniques.
Mahmood Abdulhameed Ahmed +3 more
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Generative Adversarial Networks (GANs)
2020Deep learning has launched a profound reformation and has even been applied to many real-world tasks such as image classification (He et al. 2016), object detection (Ren et al. 2015), and image segmentation (Long et al. 2015). These tasks all fall into the scope of supervised learning, which means that large amounts of labeled data are provided for the
Xudong Mao, Qing Li
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Generative Adversarial Networks (GANs) in Computer-Generated Imagery
2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI), 2023Sannan Ahmad Shah +4 more
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