Results 111 to 120 of about 43,961 (294)

Regional attention generative adversarial network

Electronics Letters, 2019
In this Letter, the authors propose a novel attention mechanism combined with a classical generative adversarial network (GAN) model to improve the visual quality of generated samples. This novel attention model is named regional attention GAN.
Wei Wang, Haifeng Hu, Yi Huang, Chongchong Ruan, Dihu Chen   +4 more
doaj   +1 more source

Inverse Design of Alloys via Generative Algorithms: Optimization and Diffusion within Learned Latent Space

Advanced Intelligent Discovery, EarlyView.
This work presents a novel generative artificial intelligence (AI) framework for inverse alloy design through operations (optimization and diffusion) within learned compact latent space from variational autoencoder (VAE). The proposed work addresses challenges of limited data, nonuniqueness solutions, and high‐dimensional spaces.
Mohammad Abu‐Mualla, Ellis Crabtree, Fredrick Michael, Yayue Pan, Jida Huang   +4 more
wiley   +1 more source

Research on intrusion detection for maritime meteorological sensor network based on balancing generative adversarial network

Tongxin xuebao, 2023
Aiming at the problem that the resources of maritime mobile terminals were limited and the network traffic was imbalanced in the MMSN (maritime meteorological sensor network) environment, which made it difficult to detect network intrusion accurately, a ...
Xin SUN, Guifu ZHANG, Hongyan XING, Wang Zenghui   +3 more
doaj  

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Exploring Generative Adversarial Networks for Text-to-Image Generation with Evolution Strategies [PDF]

, 2022
Víctor Costa, Nuno Lourenço, João Correia, Penousal Machado   +3 more
openalex   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Advances in generative adversarial network

Tongxin xuebao, 2018
Generative adversarial network (GAN) have swiftly become the focus of considerable research in generative models soon after its emergence,whose academic research and industry applications have yielded a stream of further progress along with the ...
Wanliang WANG, Zhuorong LI
doaj   +2 more sources

GAT-GAN : A Graph-Attention-based Time-Series Generative Adversarial Network [PDF]

, 2023
Srikrishna Iyer, Teng Teck Hou
openalex   +1 more source

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, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source