Results 91 to 100 of about 222,396 (351)

3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions [PDF]

IEEE International Conference on Computer Vision, 2019
In this paper, we propose a novel generative adversarial network (GAN) for 3D point clouds generation, which is called tree-GAN. To achieve state-of-the-art performance for multi-class 3D point cloud generation, a tree-structured graph convolution ...
Dong Wook Shu, Sung Woo Park, Junseok Kwon   +2 more
semanticscholar   +1 more source

Multiparameter Closed‐Loop Control to Advance Reliability of Aerosol Jet Printing

Advanced Materials Technologies, EarlyView.
A multiparameter framework for closed‐loop control of aerosol jet printing is developed to improve process consistency over both short and long timescales. By creating a control architecture responsive to the dynamic behavior of the printing system, variability in functional electrical properties is significantly reduced over extended duration printing,
Andrew J. Schwartz, Jeremy D. Rurup, Ethan B. Secor   +2 more
wiley   +1 more source

MedFusionGAN: multimodal medical image fusion using an unsupervised deep generative adversarial network [PDF]

, 2023
Mojtaba Safari, Ali Fatemi, Louis Archambault   +2 more
openalex   +1 more source

Inverse Design in Nanophotonics via Representation Learning

Advanced Optical Materials, EarlyView.
This review frames machine learning (ML) in nanophotonics through a classification based on where ML is applied. We categorize methods as either output‐side, which create differentiable surrogates for solving Maxwell's partial differential equations (PDEs), or input‐side, which learn compact representations of device geometry.
Reza Marzban, Ali Adibi, Raphaël Pestourie   +2 more
wiley   +1 more source

Entangling Quantum Generative Adversarial Networks

Physical Review Letters, 2022
Generative adversarial networks (GANs) are one of the most widely adopted semisupervised and unsupervised machine learning methods for high-definition image, video, and audio generation. In this work, we propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN) that overcomes some limitations of ...
Murphy Yuezhen Niu, Alexander Zlokapa, Michael Broughton, Sergio Boixo, Masoud Mohseni, Vadim Smelyanskyi, Hartmut Neven   +6 more
openaire   +4 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

STA-GAN: A Spatio-Temporal Attention Generative Adversarial Network for Missing Value Imputation in Satellite Data [PDF]

, 2022
Shuyu Wang, Wengen Li, Siyun Hou, Jihong Guan, Jiamin Yao   +4 more
openalex   +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

Physical adversarial attack in artificial intelligence of things

IET Communications
With the continuous development of wireless communication and artificial intelligence technology, Internet of Things (IoT) technology has made great progress. Deep learning methods are currently used in IoT technology, but deep neural networks (DNNs) are
Xin Ma, Kai Yang, Chuanzhen Zhang, Hualing Li, Xin Zheng   +4 more
doaj   +1 more source

Super‐resolution with adversarial loss on the feature maps of the generated high‐resolution image

Electronics Letters, 2022
Recent studies on image super‐resolution make use of Generative Adversarial Networks to generate the high‐resolution image counterpart of the low‐resolution input. However, while being able to generate sharp high‐resolution images, Generative Adversarial
I. Imanuel, S. Lee
doaj   +1 more source