Automatic Screening of COVID-19 Using an Optimized Generative Adversarial Network
, 2021 The quick spread of coronavirus disease (COVID-19) has resulted in a global pandemic and more than fifteen million confirmed cases. To battle this spread, clinical imaging techniques, for example, computed tomography (CT), can be utilized for diagnosis ...
Murugan, R +3 more
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Generative adversarial network for predictive maintenance of a packaging machine [PDF]
, 2022 openGenerative models have been designed to discover and learn the latent structure of the input data in order to generate new samples based on the regularities discovered in the data.
RASETTA, ADRIANO
core
A Review of GAN-Based Super-Resolution Reconstruction for Optical Remote Sensing Images
Remote Sensing, 2023 High-resolution images have a wide range of applications in image compression, remote sensing, medical imaging, public safety, and other fields. The primary objective of super-resolution reconstruction of images is to reconstruct a given low-resolution ...
Xuan Wang +3 more
doaj +1 more source
Generative Adversarial Networks in Retinal Image Classification
Applied Sciences, 2023 The recent introduction of generative adversarial networks has demonstrated remarkable capabilities in generating images that are nearly indistinguishable from real ones.
Francesco Mercaldo +4 more
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Facial expression transfer using generative adversarial network : a review [PDF]
, 2020 There is high demand of realistic facial expression in current computer graphics and multimedia research. Realistic and accurate facial expression can guarantee the animated character to deliver the expression correctly.
Mohd. Suaib, Norhaida +1 more
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An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs
Applied Sciences, 2020 Medical image segmentation is a classic challenging problem. The segmentation of parts of interest in cardiac medical images is a basic task for cardiac image diagnosis and guided surgery.
Xiaochang Wu, Xiaolin Tian
doaj +1 more source
Generation of novel Diels–Alder reactions using a generative adversarial network
, 2022 Deep learning has enormous potential in the chemical and pharmaceutical fields, and generative adversarial networks (GANs) in particular have exhibited remarkable performance in the field of molecular generation as generative models.
Yejian, Wu +9 more
core +1 more source
Structured Generative Adversarial Networks
CoRR, 2017 We study the problem of conditional generative modeling based on designated semantics or structures. Existing models that build conditional generators either require massive labeled instances as supervision or are unable to accurately control the semantics of generated samples.
Zhijie Deng +6 more
openaire +3 more sources
Application of deep learning in recognition of accrued earnings management
Heliyon, 2023 We choose the sample data in Chinese capital market to compare the measurement effect of earnings management with Deep Belief Network, Deep Convolution Generative Adversarial Network, Generalized Regression Neural Network and modified Jones model by ...
Jia Li, Zhoutianyang Sun
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Deconstructing Generative Adversarial Networks [PDF]
IEEE Transactions on Information Theory, 2020 We deconstruct the performance of GANs into three components: 1. Formulation: we propose a perturbation view of the population target of GANs. Building on this interpretation, we show that GANs can be viewed as a generalization of the robust statistics framework, and propose a novel GAN architecture, termed as Cascade GANs, to provably recover ...
Banghua Zhu, Jiantao Jiao, David Tse
openaire +2 more sources