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Sep 10, 2021 · This approach considers the changing factors of power equipment and takes it as the conditional distribution of simulation data during training.
This paper proposes and evaluates a novel solution to fill this gap using a Generative Adversarial Network (GAN) based model, ShipGAN, to translate the ...
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Jun 23, 2023 · We propose to train the GAN by an empirical Bayes-like method by treating the discriminator as a hyper-parameter of the posterior distribution ...
Mar 11, 2024 · GAN(Generative Adversarial Network) represents a cutting-edge approach to generative modeling within deep learning, often leveraging ...
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative AI.
Oct 16, 2022 · These preliminary studies show that neural networks can learn the relationship between structural geometry and magnetic field distribution.
Jul 19, 2019 · Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities ...
Mar 6, 2019 · A Generative-Adversarial Network (GAN) based on convolutional neural networks is used to simulate the production of pairs of jets at the LHC.
Feb 25, 2022 · Simulated trajectories provide a relevant alternative to develop methods based on synthetic data; for example, to correct bias in home range ...
Jun 3, 2024 · Generative Adversarial Networks is an approach to generative modeling that makes a new set of data based on training data that look similar.