Results 61 to 70 of about 16,046 (295)

Information Transmission Strategies for Self‐Organized Robotic Aggregation

Advanced Robotics Research, EarlyView.
In this review, we discuss how information transmission influences the neighbor‐based self‐organized aggregation of swarm robots. We focus specifically on local interactions regarding information transfer and categorize previous studies based on the functions of the information exchanged.
Shu Leng, Zihan Li, Yifeng Shao, Jinhao Liu, Li Yu, Shuguang Li   +5 more
wiley   +1 more source

Defect Enhancement Generative Adversarial Network for Enlarging Data Set of Microcrack Defect

IEEE Access, 2019
This paper presents a micro defect data set expansion method focuses on the microcrack defect of magnetic ring. Deep neural networks require a mass of training samples to be fully optimized.
Song Lin, Zhiyong He, Lining Sun
doaj   +1 more source

Synthetic demand data generation for individual electricity consumers: Generative Adversarial Networks (GANs)

, 2022
Load modeling is one of the crucial tasks for improving smart grids’ energy efficiency. Among many alternatives, machine learning-based load models have become popular in applications and have shown outstanding performance in recent years.
Yilmaz, B., Korn, Ralf
core   +1 more source

Solid Harmonic Wavelet Bispectrum for Image Analysis

Advanced Science, EarlyView.
The Solid Harmonic Wavelet Bispectrum (SHWB), a rotation‐ and translation‐invariant descriptor that captures higher‐order (phase) correlations in signals, is introduced. Combining wavelet scattering, bispectral analysis, and group theory, SHWB achieves interpretable, data‐efficient representations and demonstrates competitive performance across texture,
Alex Brown, Mathilda Avirett‐Mackenzie, Carolin Villforth, Georgios Exarchakis   +3 more
wiley   +1 more source

A Future Picture: A Review of Current Generative Adversarial Neural Networks in Vitreoretinal Pathologies and Their Future Potentials

Biomedicines
Machine learning has transformed ophthalmology, particularly in predictive and discriminatory models for vitreoretinal pathologies. However, generative modeling, especially generative adversarial networks (GANs), remains underexplored.
Raheem Remtulla, Adam Samet, Merve Kulbay, Arjin Akdag, Adam Hocini, Anton Volniansky, Shigufa Kahn Ali, Cynthia X. Qian   +7 more
doaj   +1 more source

Understanding Generative Adversarial Networks

, 2023
Generative adversarial networks (GANs) are a new technology impacting social, policy, and security discourses. The rise of GANs enables the creation of artificially generated hyper-realistic human faces.
Ludwig, Peyton
core  

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

Generative Adversarial Network (GAN) to Generate Realistic Images

International Journal for Research in Applied Science and Engineering Technology, 2023
Abstract: Generative Adversarial Networks (GANs) have rapidly become a focal point of research due to their ability to generate realistic images. First introduced in 2014, GANs have been applied in a multitude of fields such as computer vision and natural language processing, yielding impressive results.
Sahil Lamba, Himanshu ., Rohit Kumar Singh, Er. Kamal Soni   +3 more
openaire   +1 more source

Lifelong Twin Generative Adversarial Networks [PDF]

, 2021
In this paper, we propose a new continuously learning generative model, called the Lifelong Twin Generative Adversarial Networks (LT-GANs). LT-GANs learns a sequence of tasks from several databases and its architecture consists of three components: two ...
Ye, Fei, Bors, Adrian Gheorghe
core  

From Label‐Free Multiphoton Imaging to Pathological Reports: A Vision‐Language Breast Cancer Margin Pathological Diagnosis System

Advanced Science, EarlyView.
MarginPath is a novel vision‐language system that automates breast cancer margin assessment using a single label‐free multiphoton microscopy image. By integrating tumor‐associated collagen signatures with virtual H&E imaging, it generates accurate margin heatmaps and comprehensive diagnostic reports.
Shu Wang, Jingze Su, Xiahui Han, Deyong Kang, Xiao Zhang, Fei Xu, Changzu Liu, Junlin Pan, Xingfu Wang, Qiaohui Zhan, Aimin Wang, Feng Huang, Heping Cheng, Wenxi Liu, Ruolan Lin, Jianxin Chen   +15 more
wiley   +1 more source