Results 61 to 70 of about 2,921,969 (298)

Generative Adversarial Networks in Speech Enhancement: A Survey

IEEE Access
Generative adversarial networks are a powerful type of model in deep learning. They have been successfully applied within different domains. This review focuses on the usage of generative adversarial networks for speech enhancement.
Justina Ramonaite, Grazina Korvel, Gintautas Tamulevicius   +2 more
doaj   +1 more source

Ensembles of Generative Adversarial Networks

CoRR, 2016
accepted NIPS 2016 Workshop on Adversarial ...
Yaxing Wang, Lichao Zhang 0001, Joost van de Weijer 0001   +2 more
openaire   +2 more sources

Controllable Generative Adversarial Network [PDF]

IEEE Access, 2019
A fully revised version of this paper is published in IEEE Access.
Minhyeok Lee, Junhee Seok
openaire   +4 more sources

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Study of image reconstruction efficiency in a single-pixel imaging method using generative adversarial networks

Компьютерная оптика
Single-pixel imaging is a promising image acquisition method that provides an alternative to traditional imaging methods using multi-pixel matrices. However, algorithmic image reconstruction from measurements of a single-pixel camera is a non-trivial ...
D.V. Babukhin, A.A. Reutov, D.V. Sych
doaj   +1 more source

Attribute-Aware Generative Design With Generative Adversarial Networks

IEEE Access, 2020
The designers' tendency to adhere to a specific mental set and heavy emotional investment in their initial ideas often limit their ability to innovate during the design ideation process.
Chenxi Yuan, Mohsen Moghaddam
doaj   +1 more source

Using Novelty Seeking Reward Evolution Strategies to Train Generative Adversarial Networks [PDF]

, 2018
Generative Adversarial Networks (GANs) are a subclass of deep generative models that aim to implicitly learn to model a data distribution. While GANs have gained wide research attention, and achieved much success, when trained with first-order stochastic
Jabr, Khaled
core  

Robust Generative Adversarial Network

CoRR, 2020
Generative adversarial networks (GANs) are powerful generative models, but usually suffer from instability and generalization problem which may lead to poor generations. Most existing works focus on stabilizing the training of the discriminator while ignoring the generalization properties.
Shufei Zhang, Zhuang Qian, Kaizhu Huang, Jimin Xiao, Yuan He   +4 more
openaire   +3 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

An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs

Applied Sciences, 2020
Medical image segmentation is a classic challenging problem. The segmentation of parts of interest in cardiac medical images is a basic task for cardiac image diagnosis and guided surgery.
Xiaochang Wu, Xiaolin Tian
doaj   +1 more source