Results 61 to 70 of about 124,149 (320)
Attribute-Aware Generative Design With Generative Adversarial Networks
IEEE Access, 2020 The designers' tendency to adhere to a specific mental set and heavy emotional investment in their initial ideas often limit their ability to innovate during the design ideation process.
Chenxi Yuan, Mohsen Moghaddam
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Modular Generative Adversarial Networks [PDF]
, 2018 Existing methods for multi-domain image-to-image translation (or generation) attempt to directly map an input image (or a random vector) to an image in one of the output domains. However, most existing methods have limited scalability and robustness, since they require building independent models for each pair of domains in question.
Zequn Jie +3 more
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Targeted Speech Adversarial Example Generation With Generative Adversarial Network [PDF]
IEEE Access, 2020 Although neural network-based speech recognition models have enjoyed significant success in many acoustic systems, they are susceptible to be attacked by the adversarial examples. In this work, we make first step towards using generative adversarial network (GAN) for constructing the targeted speech adversarial examples.
Donghua Wang +4 more
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A generative adversarial network to Reinhard stain normalization for histopathology image analysis
Ain Shams Engineering JournalHistopathology image analysis is paramount importance for accurate diagnosing diseases and gaining insight into tissue properties. The significant challenge of staining variability continues.
Afnan M. Alhassan
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Review of Application of Generative Adversarial Networks in Image Restoration [PDF]
Jisuanji kexue yu tansuoWith the rapid development of generative adversarial networks, many image restoration problems that are difficult to solve based on traditional methods have gained new research approaches.
GONG Ying, XU Wentao, ZHAO Ce, WANG Binjun
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Beautification of images by generative adversarial networks
Journal of Vision, 2023 Finding the properties underlying beauty has always been a prominent yet difficult problem. However, new technological developments have often aided scientific progress by expanding the scientists' toolkit. Currently in the spotlight of cognitive neuroscience and vision science are deep neural networks.
Music, Amar +2 more
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A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems
International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam +2 more
wiley +1 more source
Adversarial Variational Optimization of Non-Differentiable Simulators [PDF]
, 2019 Complex computer simulators are increasingly used across fields of science as generative models tying parameters of an underlying theory to experimental observations.
Cranmer, Kyle +2 more
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Generative Adversarial Networks GAN Overview
Jisuanji kexue yu tansuo, 2020 As a new unsupervised learning algorithm framework, generative adversarial networks (GAN) has been favored by more and more researchers, and it has become a research hotspot. GAN is inspired by the two-person zero-sum game theory in game theory.
LIANG Junjie, WEI Jianjing, JIANG Zhengfeng
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Entangling Quantum Generative Adversarial Networks
Physical Review Letters, 2022 Generative adversarial networks (GANs) are one of the most widely adopted semisupervised and unsupervised machine learning methods for high-definition image, video, and audio generation. In this work, we propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN) that overcomes some limitations of ...
Murphy Yuezhen Niu +6 more
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