Results 51 to 60 of about 3,042,897 (294)
Generative Adversarial Neural Networks for Denoising Coherent Multidimensional Spectra [PDF]
, 2022 Ultrafast spectroscopy often involves measuring weak signals and long data acquisition times. Spectra are typically collected as a “pump-probe” spectrum by measuring differences in intensity across laser shots.
Ziareena, Al-Mualem +3 more
core +1 more source
On the "steerability" of generative adversarial networks
CoRR, 2019 An open secret in contemporary machine learning is that many models work beautifully on standard benchmarks but fail to generalize outside the lab. This has been attributed to biased training data, which provide poor coverage over real world events.
Ali Jahanian 0002 +2 more
openaire +3 more sources
Stacked Generative Adversarial Networks [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 CVPR 2017, camera-ready ...
Xun Huang 0002 +4 more
openaire +3 more sources
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
doaj +1 more source
Recent Generative Adversarial Approach in Face Aging and Dataset Review
IEEE Access, 2022 Many studies have been conducted in the field of face aging, from approaches that use pure image-processing algorithms, to those that use generative adversarial networks.
Hady Pranoto +3 more
doaj +1 more source
NAG: Network for Adversary Generation [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Adversarial perturbations can pose a serious threat for deploying machine learning systems. Recent works have shown existence of image-agnostic perturbations that can fool classifiers over most natural images. Existing methods present optimization approaches that solve for a fooling objective with an imperceptibility constraint to craft the ...
Konda Reddy Mopuri +3 more
openaire +2 more sources
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 +3 more sources
Role of Structural Disorder on Phonon Transport and Thermal Conductivity in Li6PS5Br
Advanced Functional Materials, EarlyView.Solid electrolytes are fast ion and slow heat conductors. Although both transport properties are governed by lattice dynamics and atomic structure, their interplay remains poorly understood. Here, we study this interrelation in anion‐ordered and disordered Li6PS5Br.
Lukas Ketter +4 more
wiley +1 more source
A progressive growing of conditional generative adversarial networks model
Dianxin kexue, 2023 Progressive growing of generative adversarial networks (PGGAN) is an adversarial network model that can generate high-resolution images.However, when the categories of samples are unbalanced, or the categories of samples are too similar or too dissimilar,
Hui MA, Ruiqin WANG, Shuai YANG
doaj +2 more sources
Generative Adversarial Networks in Speech Enhancement: A Survey
IEEE AccessGenerative adversarial networks are a powerful type of model in deep learning. They have been successfully applied within different domains. This review focuses on the usage of generative adversarial networks for speech enhancement.
Justina Ramonaite +2 more
doaj +1 more source