Results 41 to 50 of about 43,961 (294)

Generative Adversarial Networks: An Overview [PDF]

IEEE Signal Processing Magazine, 2018
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image ...
Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, Anil A. Bharath   +5 more
openaire   +4 more sources

Generative Adversarial Networks: A Primer for Radiologists

RadioGraphics, 2021
Artificial intelligence techniques involving the use of artificial neural networks-that is, deep learning techniques-are expected to have a major effect on radiology. Some of the most exciting applications of deep learning in radiology make use of generative adversarial networks (GANs).
Jelmer M. Wolterink, Anirban Mukhopadhyay, Tim Leiner, Thomas J. Vogl, Andreas M. Bucher, Ivana Išgum   +5 more
openaire   +5 more sources

Attentively Conditioned Generative Adversarial Network for Semantic Segmentation

IEEE Access, 2020
Generative Adversarial Network has proven to produce state-of-the-art results by framing a generative modeling task into a supervised learning problem. In this paper, we propose Attentively Conditioned Generative Adversarial Network (ACGAN) for semantic ...
Ariyo Oluwasanmi, Muhammad Umar Aftab, Akeem Shokanbi, Jehoiada Jackson, Bulbula Kumeda, Zhiquang Qin   +5 more
doaj   +1 more source

Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Ehécatl

Applied Sciences, 2023
This paper introduces a novel and robust data-driven algorithm designed for Aircraft Trajectory Prediction (ATP). The approach employs a Neural Network architecture to predict future aircraft trajectories, utilizing input variables such as latitude ...
Seyed Mohammad Hashemi, Seyed Ali Hashemi, Ruxandra Mihaela Botez, Georges Ghazi   +3 more
doaj   +1 more source

Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐Entropy Thermoelectrics

Advanced Materials, EarlyView.
An active learning framework is introduced for the accelerated discovery of high‐entropy chalcogenides with superior thermoelectric performance. Only 80 targeted syntheses, selected from 16206 possible combinations, led to three high‐performance compositions, demonstrating the remarkable efficiency of data‐driven guidance in experimental materials ...
Hanhwi Jang, Wooseok Lee, Hwa‐Jung Kim, Sohyang Cha, Hosun Shin, Won Bo Lee, Min‐Wook Oh, Yeon Sik Jung, YongJoo Kim   +8 more
wiley   +1 more source

Organic Electrochemical Transistors for Neuromorphic Devices and Applications

Advanced Materials, EarlyView.
Organic electrochemical transistors are emerging as promising platforms for neuromorphic devices that emulate neuronal and synaptic activities and can seamlessly integrate with biological systems. This review focuses on resultant organic artificial neurons, synapses, and integrated devices, with an emphasis on their ability to perform neuromorphic ...
Kexin Xiang, Jiajun Song, Hong Liu, Junxin Chen, Feng Yan   +4 more
wiley   +1 more source

PAMSGAN: Pyramid Attention Mechanism-Oriented Symmetry Generative Adversarial Network for Motion Image Deblurring

IEEE Access, 2021
Motion blur is a common problem in optical imaging, which is caused by the relative displacement between the subject and the camera in the exposure process of the camera.
Zhenfeng Zhang
doaj   +1 more source

Materials and System Design for Self‐Decision Bioelectronic Systems

Advanced Materials, EarlyView.
This review highlights how self‐decision bioelectronic systems integrate sensing, computation, and therapy into autonomous, closed‐loop platforms that continuously monitor and treat diseases, marking a major step toward intelligent, self‐regulating healthcare technologies.
Qiankun Zeng, Hong Liu, Yongheng Zhang, Youbin Zheng, Xuyin Ding, Mengni Zhu, Guoyue Shi, Yan Wang, Hossam Haick, Min Zhang   +9 more
wiley   +1 more source

Phylogenetic inference using Generative Adversarial Networks

Bioinformatics, 2022
AbstractMotivationThe application of machine learning approaches in phylogenetics has been impeded by the vast model space associated with inference. Supervised machine learning approaches require data from across this space to train models. Because of this, previous approaches have typically been limited to inferring relationships among unrooted ...
Megan L. Smith, Matthew W. Hahn
openaire   +2 more sources

From the Discovery of the Giant Magnetocaloric Effect to the Development of High‐Power‐Density Systems

Advanced Materials Technologies, EarlyView.
The article overviews past and current efforts on caloric materials and systems, highlighting the contributions of Ames National Laboratory to the field. Solid‐state caloric heat pumping is an innovative method that can be implemented in a wide range of cooling and heating applications.
Agata Czernuszewicz, Yaroslav Mudryk, Jun Cui, Lucas Griffith, Duane D. Johnson, Julie Slaughter   +5 more
wiley   +1 more source