Results 11 to 20 of about 45,910 (264)
Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs) [PDF]
IEEE Transactions on Cybernetics, 2021 Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i.e.
Cheng He +4 more
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I-GANs for Infrared Image Generation
Complexity, 2021 The making of infrared templates is of great significance for improving the accuracy and precision of infrared imaging guidance. However, collecting infrared images from fields is difficult, of high cost, and time-consuming.
Bing Li +4 more
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LDDMM Meets GANs: Generative Adversarial Networks for Diffeomorphic Registration
Jornada de Jóvenes Investigadores del I3A, 2021 In this work, we propose an unsupervised adversarial learning LDDMM method for 3D mono-modal images based on Generative Adversarial Networks. We have successfully implemented two models with stationary and EPDiff constrained non-stationary parameterizations of diffeomorphisms.
Ramón Júlvez, Ubaldo +2 more
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PL-GAN: Path Loss Prediction Using Generative Adversarial Networks
IEEE Access, 2022 Accurate prediction of path loss is essential for the design and optimization of wireless communication networks. Existing path loss prediction methods typically suffer from the trade-off between accuracy and computational efficiency. In this paper, we present a deep learning based approach with clear advantages over the existing ones.
Ahmed Marey +3 more
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MB-GAN: Microbiome Simulation via Generative Adversarial Network [PDF]
GigaScience, 2019 AbstractSimulation is a critical component of experimental design and evaluation of analysis methods in microbiome association studies. However, statistically modeling the microbiome data is challenging since that the complex structure in the real data is difficult to be fully represented by statistical models.
Ruichen Rong +7 more
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Generative Adversarial Network for Medical Images (MI-GAN) [PDF]
Journal of Medical Systems, 2018 Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, these algorithms require large dataset for training. However, deep learning algorithms lack generalization and suffer from over-fitting whenever trained on small dataset, especially when one is dealing ...
Talha Iqbal, Hazrat Ali
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TextGAIL: Generative Adversarial Imitation Learning for Text Generation
, 2021 Generative Adversarial Networks (GANs) for text generation have recently received many criticisms, as they perform worse than their MLE counterparts. We suspect previous text GANs' inferior performance is due to the lack of a reliable guiding signal in ...
Li, Lei, Wu, Qingyang, Yu, Zhou
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Dynamics of Fourier Modes in Torus Generative Adversarial Networks
Mathematics, 2021 Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating fully synthetic samples of a desired phenomenon with a high resolution.
Ángel González-Prieto +3 more
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PEGANs: Phased Evolutionary Generative Adversarial Networks with Self-Attention Module
Mathematics, 2022 Generative adversarial networks have made remarkable achievements in generative tasks. However, instability and mode collapse are still frequent problems.
Yu Xue +3 more
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Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)
Signals, 2021 Generative Adversarial Networks (GANs) have gained significant attention in recent years, with impressive applications highlighted in computer vision, in particular. Compared to such examples, however, there have been more limited applications of GANs to
Jaleh Zand, Stephen Roberts
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