Results 91 to 100 of about 4,829,196 (302)

Generative Multi-Adversarial Networks

CoRR, 2016
Generative adversarial networks (GANs) are a framework for producing a generative model by way of a two-player minimax game. In this paper, we propose the \emph{Generative Multi-Adversarial Network} (GMAN), a framework that extends GANs to multiple discriminators.
Ishan P. Durugkar, Ian Gemp, Sridhar Mahadevan   +2 more
openaire   +4 more sources

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Modular Generative Adversarial Networks [PDF]

, 2018
Existing methods for multi-domain image-to-image translation (or generation) attempt to directly map an input image (or a random vector) to an image in one of the output domains. However, most existing methods have limited scalability and robustness, since they require building independent models for each pair of domains in question.
Bo Zhao 0032, Bo Chang 0002, Zequn Jie, Leonid Sigal   +3 more
openaire   +2 more sources

SemanticST: A Scalable Multi‐Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi‐Sample Integration in Spatial Transcriptomics

Advanced Science, EarlyView.
Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi, Ahmadreza Argha, Nona Farbehi, Ivan Bakhshayeshi, Thantrira Porntaveetus, Youqiong Ye, Nigel H. Lovell, Hamid Alinejad‐Rokny   +7 more
wiley   +1 more source

A generative adversarial network with multiscale and attention mechanisms for underwater image enhancement

Scientific Reports
Underwater images collected are often of low clarity and suffer from severe color distortion due to the marine environment and Illumination conditions.
Liquan Zhao, Yuda Li, Tie Zhong
doaj   +1 more source

Network Intrusion Detection Based on Spatial Features and GenerativeAdversarial Networks [PDF]

Zhengzhou Daxue xuebao. Gongxue ban
Address issues such as the inadequate consideration of inter-feature correlations in existing intrusion detection methods and the need for improved detection accuracy on high-dimensional discrete datasets, a network intrusion detection method MBGAN ...
ZHANG Zhen, ZHOU Yicheng, TIAN Hongpeng
doaj   +1 more source

cardiGAN: A generative adversarial network model for design and discovery of multi principal element alloys

, 2022
cardiGAN: A generative adversarial network model for design and discovery of multi principal element ...
WT Nash (17096659), Y Qiu (6848966), SP O'Brien (16862442), RK Gupta (9892412), Nick Birbilis (14681953), Z Li (774829)   +5 more
core  

Neuromorphic Devices and Computing for Sensing, Memory, and Control

Advanced Science, EarlyView.
This review introduces neuromorphic devices made from diverse materials. These devices mimic neuronal functions and architectures and, when integrated with artificial or biological computing, can form closed loops with neurons for pressure, optical, acoustic, and biochemical sensing and modulation.
Zhengguang Zhu, Nicholas Schaffer, Xiao Yang   +2 more
wiley   +1 more source

Physical adversarial attack in artificial intelligence of things

IET Communications
With the continuous development of wireless communication and artificial intelligence technology, Internet of Things (IoT) technology has made great progress. Deep learning methods are currently used in IoT technology, but deep neural networks (DNNs) are
Xin Ma, Kai Yang, Chuanzhen Zhang, Hualing Li, Xin Zheng   +4 more
doaj   +1 more source

VAE-WACGAN: An Improved Data Augmentation Method Based on VAEGAN for Intrusion Detection

Sensors
To address the class imbalance issue in network intrusion detection, which degrades performance of intrusion detection models, this paper proposes a novel generative model called VAE-WACGAN to generate minority class samples and balance the dataset. This
Wuxin Tian, Yanping Shen, Na Guo, Jing Yuan, Yanqing Yang   +4 more
doaj   +1 more source