Results 81 to 90 of about 60,338 (274)
Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN
, 2017 We propose a novel technique to make neural network robust to adversarial examples using a generative adversarial network. We alternately train both classifier and generator networks.
Han, Sungyeob +2 more
core
Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook
Advanced Intelligent Discovery, EarlyView.Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang +4 more
wiley +1 more source
CAAI Transactions on Intelligence Technology, EarlyView., 2023 Abstract Autonomous vehicles are required to operate in an uncertain environment. Recent advances in computational intelligence techniques make it possible to understand driving scenes in various environments by using a semantic segmentation neural network, which assigns a class label to each pixel.
Yining Hua +4 more
wiley +1 more source
Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art
Machine Learning: Science and TechnologyGenerative adversarial networks (GANs) have rapidly emerged as powerful tools for generating realistic and diverse data across various domains, including computer vision and other applied areas, since their inception in 2014.
Tanujit Chakraborty +4 more
doaj +1 more source
Advanced Intelligent Discovery, EarlyView.Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto +3 more
wiley +1 more source
Super-Resolution of Remote Sensing Images via a Dense Residual Generative Adversarial Network
Remote Sensing, 2019 Single image super-resolution (SISR) has been widely studied in recent years as a crucial technique for remote sensing applications. In this paper, a dense residual generative adversarial network (DRGAN)-based SISR method is proposed to promote the ...
Wen Ma, Zongxu Pan, Feng Yuan, Bin Lei
doaj +1 more source
Generative Adversarial Network (GAN)-Based Autonomous Penetration Testing for Web Applications. [PDF]
Sensors (Basel), 2023 Chowdhary A, Jha K, Zhao M.
europepmc +1 more source
KG-GAN: Knowledge-Guided Generative Adversarial Networks
, 2019 Can generative adversarial networks (GANs) generate roses of various colors given only roses of red petals as input? The answer is negative, since GANs' discriminator would reject all roses of unseen petal colors. In this study, we propose knowledge-guided GAN (KG-GAN) to fuse domain knowledge with the GAN framework.
Chang, Che-Han +3 more
openaire +2 more sources
Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art
Advanced Intelligent Discovery, EarlyView.Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser +6 more
wiley +1 more source
Screening of adolescent idiopathic scoliosis using generative adversarial network (GAN) inversion method in chest radiographs. [PDF]
PLoS One, 2023 Lee JS +8 more
europepmc +1 more source