Results 231 to 240 of about 26,076 (260)

Generative Adversarial Networks for Energy-Aware IoT Intrusion Detection: Comprehensive Benchmark Analysis of GAN Architectures with Accuracy-per-Joule Evaluation. [PDF]

Sensors (Basel)
Ioannou I, Vassiliou V.
europepmc   +1 more source

Enhancing buckwheat maturity classification with generative adversarial networks for spectroscopy data augmentation. [PDF]

Front Plant Sci
Wang H, Che X, Nan J, Miao Y, Wang Y, Zhang W, Li F, Han J.   +7 more
europepmc   +1 more source

Enhancing art creation through AI-based generative adversarial networks in educational auxiliary system. [PDF]

Sci Rep
He Y, Zhang S.
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Robust generative adversarial network

Machine Learning, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shufei Zhang, Zhuang Qian, Kaizhu Huang, Rui Zhang 0012, Jimin Xiao, Yuan He, Canyi Lu   +6 more
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Interpretable Generative Adversarial Networks

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
Learning a disentangled representation is still a challenge in the field of the interpretability of generative adversarial networks (GANs). This paper proposes a generic method to modify a traditional GAN into an interpretable GAN, which ensures that filters in an intermediate layer of the generator encode disentangled localized visual concepts.
Chao Li 0028, Kelu Yao, Jin Wang 0039, Boyu Diao, Yongjun Xu 0001, Quanshi Zhang   +5 more
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Evolutionary Generative Adversarial Networks [PDF]

IEEE Transactions on Evolutionary Computation, 2019
14 pages, 8 ...
Dacheng Tao, Xin Yao, Chang Xu
exaly   +3 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
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Generative Adversarial Networks: An Overview [PDF]

IEEE Signal Processing Magazine, 2018
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image ...
Anil Bharath, Kai Arulkumaran, Antonia Creswell   +2 more
exaly   +4 more sources

Generative Adversarial Networks for Classification

2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2017
Our team is reviewing tools and techniques that enable rapid prototyping. Generative Adversarial Networks (GANs) have been shown to reduce training requirements for detection problems. GANs compete generative and discriminative classifiers to improve detection performance.
Steven A. Israel, J. H. Goldstein, Jeffrey S. Klein, James Talamonti, Franklin Tanner, Shane Zabel, Philip A. Sallee, Lisa McCoy   +7 more
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