Results 51 to 60 of about 16,046 (295)

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

Generative adversarial networks: an overview [PDF]

, 2018
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this by deriving backpropagation signals through a competitive process involving a pair of networks.
Antonia Creswell, Kai Arulkumaran, Creswell, Antonia, Anil A. Bharath, Biswa Sengupta, Arulkumaran, Kai, While, Tom, Dumoulin, Vincent, Sengupta, Biswa, Bharath, Anil A, Tom White, Vincent Dumoulin   +11 more
core   +1 more source

Training Generative Adversarial Networks via Stochastic Nash Games

, 2023
Generative adversarial networks (GANs) are a class of generative models with two antagonistic neural networks: a generator and a discriminator. These two neural networks compete against each other through an adversarial process that can be modeled as a ...
Franci, Barbara, Franci, B., Franci, B. (author), Grammatico, Sergio, Grammatico, S., Grammatico, S. (author)   +5 more
core   +1 more source

Inverse Design of Amorphous Materials With Targeted Properties

Advanced Materials, EarlyView.
AMDEN is a diffusion model framework for the inverse design of amorphous materials with targeted properties. By incorporating Hamiltonian Monte Carlo refinement into the denoising process, the framework overcomes the challenge of generating thermally relaxed disordered structures.
Jonas A. Finkler, Yan Lin, Tao Du, Jilin Hu, Morten M. Smedskjaer   +4 more
wiley   +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  

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

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

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

Stable Imitation of Multigait and Bipedal Motions for Quadrupedal Robots Over Uneven Terrains

Advanced Robotics Research, EarlyView.
How are quadrupedal robots empowered to execute complex navigation tasks, including multigait and bipedal motions? Challenges in stability and real‐world adaptation persist, especially with uneven terrains and disturbances. This article presents an imitation learning framework that enhances adaptability and robustness by incorporating long short‐term ...
Erdong Xiao, Yinzhao Dong, Ji Ma, Peng Lu   +3 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