Results 41 to 50 of about 45,910 (264)

GANscan: continuous scanning microscopy using deep learning deblurring

Light: Science & Applications, 2022
In order to speed up the microscopy acquisition process, we developed a method, termed GANscan, in which videos are recorded as the stage is moving at high speeds.
Michael John Fanous, Gabriel Popescu
doaj   +1 more source

GANs and Artificial Facial Expressions in Synthetic Portraits

Big Data and Cognitive Computing, 2021
Generative adversarial networks (GANs) provide powerful architectures for deep generative learning. GANs have enabled us to achieve an unprecedented degree of realism in the creation of synthetic images of human faces, landscapes, and buildings, among ...
Pilar Rosado, Rubén Fernández, Ferran Reverter   +2 more
doaj   +1 more source

Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning

, 2017
Recent advances in Generative Adversarial Learning allow for new modalities of image super-resolution by learning low to high resolution mappings.
Bosch, Marc, Gifford, Christopher M., Rodriguez, Pedro A.   +2 more
core   +1 more source

2D Nanomaterials Toward Function‐Ready Superlubricity in Advanced Microsystems

Advanced Materials, EarlyView.
A unified framework links structural and transformation superlubricity with microsystem functions and deployment requirements. Mechanisms, device architectures, integration strategies, AI‐guided discovery, and benchmarking protocols are connected to define function‐ready superlubricity in advanced microsystems.
Yushan Geng, Jun Yang, Yong Yang
wiley   +1 more source

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

Investigating the effect of loss functions on single-image GAN performance

Journal of Innovative Science and Engineering
Loss functions are crucial in training generative adversarial networks (GANs) and shaping the resulting outputs. These functions, specifically designed for GANs, optimize generator and discriminator networks together but in opposite directions.
Eyyup YİLDİZ, Mehmet Erkan YUKSEL, Selcuk SEVGEN   +2 more
doaj   +1 more source

Applications of generative adversarial networks in neuroimaging and clinical neuroscience

NeuroImage, 2023
Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to the broader family of generative methods, which learn to generate realistic data with a ...
Rongguang Wang, Vishnu Bashyam, Zhijian Yang, Fanyang Yu, Vasiliki Tassopoulou, Sai Spandana Chintapalli, Ioanna Skampardoni, Lasya P. Sreepada, Dushyant Sahoo, Konstantina Nikita, Ahmed Abdulkadir, Junhao Wen, Christos Davatzikos   +12 more
doaj   +1 more source

Adaptive Density Estimation for Generative Models [PDF]

, 2019
Unsupervised learning of generative models has seen tremendous progress over recent years, in particular due to generative adversarial networks (GANs), variational autoencoders, and flow-based models.
Alahari, Karteek, Lucas, Thomas, Schmid, Cordelia, Shmelkov, Konstantin, Verbeek, Jakob   +4 more
core   +2 more sources

Learning Highly Dynamic Skills Transition for Quadruped Jumping Through Constrained Space

Advanced Robotics Research, EarlyView.
A quadruped robot masters dynamic jumps through constrained spaces with animal‐inspired moves and intelligent vision control. This hierarchical learning approach combines imitation of biological agility with real‐time trajectory planning. Although legged animals are capable of performing explosive motions while traversing confined spaces, replicating ...
Zeren Luo, Jiahui Zhang, Yimin Han, Ji Ma, Minghao Lu, Ioannis Havoutis, Peng Lu   +6 more
wiley   +1 more source

Deepfakes in Ophthalmology

Ophthalmology Science, 2021
Purpose: Generative adversarial networks (GANs) are deep learning (DL) models that can create and modify realistic-appearing synthetic images, or deepfakes, from real images.
Jimmy S. Chen, MD, Aaron S. Coyner, PhD, R.V. Paul Chan, MD, M. Elizabeth Hartnett, MD, Darius M. Moshfeghi, MD, Leah A. Owen, MD, PhD, Jayashree Kalpathy-Cramer, PhD, Michael F. Chiang, MD, MA, J. Peter Campbell, MD, MPH   +8 more
doaj   +1 more source