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Generating context-specific sports training plans by combining generative adversarial networks. [PDF]

PLoS One
Tan J, Chen J.
europepmc   +1 more source

Generative Adversarial Networks

International Journal for Research in Applied Science and Engineering Technology, 2021
Generative Adversarial Networks (GANs) are a type of deep learning techniques that have shown remarkable success in generating realistic images, videos, and other types of data.
I. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, Yoshua Bengio   +7 more
semanticscholar   +4 more sources

Generative Adversarial Networks [PDF]

ACM Computing Surveys, 2022
Zhipeng Cai, Honghui Xu, Yi Pan
exaly   +6 more sources

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f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks

Medical Image Analysis, 2019
Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein   +2 more
exaly   +2 more sources

Generative Adversarial Networks in Cardiology

Canadian Journal of Cardiology, 2022
Generative adversarial networks (GANs) are state-of-the-art neural network models used to synthesise images and other data. GANs brought a considerable improvement to the quality of synthetic data, quickly becoming the standard for data-generation tasks.
Skandarani, Youssef, Lalande, Alain, Afilalo, Jonathan, Jodoin, Pierre-Marc   +3 more
openaire   +3 more sources

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

ECCV Workshops, 2018
The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts.
Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Y. Qiao, Xiaoou Tang   +8 more
semanticscholar   +1 more source

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

Information Processing in Medical Imaging, 2017
Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection.
T. Schlegl, Philipp Seeböck, S. Waldstein, U. Schmidt-Erfurth, G. Langs   +4 more
semanticscholar   +1 more source

Generative Adversarial Networks for Face Generation: A Survey

ACM Computing Surveys, 2022
Recently, generative adversarial networks (GANs) have progressed enormously, which makes them able to learn complex data distributions in particular faces. More and more efficient GAN architectures have been designed and proposed to learn the different variations of faces, such as cross pose, age, expression, and style.
Amina Kammoun, Rim Slama, Hedi Tabia, Tarek Ouni, Mohmed Abid   +4 more
openaire   +2 more sources