Results 21 to 30 of about 4,829,196 (302)
Inverting the Generator of a Generative Adversarial Network [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2019 Under review at IEEE ...
Antonia Creswell, Anil Anthony Bharath
openaire +8 more sources
Constrained Generative Adversarial Networks [PDF]
IEEE Access, 2021 Generative Adversarial Networks (GANs) are a powerful subclass of generative models. Yet, how to effectively train them to reach Nash equilibrium is a challenge. A number of experiments have indicated that one possible solution is to bound the function space of the discriminator.
Xiaopeng Chao +4 more
openaire +3 more sources
Quantum generative adversarial networks [PDF]
Physical Review A, 2018 10 pages, 8 ...
Pierre-Luc Dallaire-Demers +1 more
openaire +3 more sources
Fuzzy Generative Adversarial Networks
CoRR, 2021 Generative Adversarial Networks (GANs) are well-known tools for data generation and semi-supervised classification. GANs, with less labeled data, outperform Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) in classification across various tasks, this shows promise for developing GANs capable of trespassing into the domain of semi ...
Ryan Nguyen +2 more
openaire +3 more sources
Triple Generative Adversarial Networks [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 We propose a unified game-theoretical framework to perform classification and conditional image generation given limited supervision. It is formulated as a three-player minimax game consisting of a generator, a classifier and a discriminator, and therefore is referred to as Triple Generative Adversarial Network (Triple-GAN).
Chongxuan Li +4 more
openaire +3 more sources
Higher-Resolution-and-Less-Noisy-Seismic-Images-An-Application-of-Generative-Adversarial-Neural-Net
, 2022 An application of generative adversarial networks to seismic data processing (resolution ehancement and denoising). This is a repository for the paper "Higher Resolution and Less Noisy Seismic Images: An Application of Generative Adversarial Neural Net" (
Lei Lin (12656614)
core +1 more source
Enhanced Face Image Super-Resolution Using Generative Adversarial Network [PDF]
, 2022 We proposed an Enhanced Face Image Generative Adversarial Network (EFGAN). Single image super-resolution (SISR) using a convolutional is often a problem in enhancing more refined texture upscaling factors. Our approach focused on mean square error (MSE),
Hardiansyah, Bagus +3 more
core +1 more source
Hyperbolic Generative Adversarial Network
CoRR, 2021 Recently, Hyperbolic Spaces in the context of Non-Euclidean Deep Learning have gained popularity because of their ability to represent hierarchical data. We propose that it is possible to take advantage of the hierarchical characteristic present in the images by using hyperbolic neural networks in a GAN architecture.
Diego Lazcano +2 more
openaire +2 more sources
Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC) [PDF]
, 2017 Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over ...
Gabriel L., Guimaraes +3 more
core +1 more source
Technology in Cancer Research & Treatment, 2022 Purpose: To overcome the imaging artifacts and Hounsfield unit inaccuracy limitations of cone-beam computed tomography, a conditional generative adversarial network is proposed to synthesize high-quality computed tomography-like images from cone-beam ...
Yun Zhang PhD +6 more
doaj +1 more source