HGAN: Hyperbolic Generative Adversarial Network
IEEE Access, 2021 Recently, Hyperbolic Spaces in the context of Non-Euclidean Deep Learning have gained popularity because of their ability to represent hierarchical data.
Diego Lazcano +2 more
doaj +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 +6 more
wiley +1 more source
Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network
Sensors(1) Background: In order to solve the problem of missing time-series data due to the influence of the acquisition system or external factors, a missing time-series data interpolation method based on random forest and a generative adversarial ...
Hongsen Ou, Yunan Yao, Yi He
doaj +1 more source
Generating Adversarial Examples with Adversarial Networks [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high ...
Xiao, Chaowei +5 more
openaire +2 more sources
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 +3 more
wiley +1 more source
Optoelectronic generative adversarial networks
Communications PhysicsRecent breakthroughs in artificial intelligence generative content technology are driving transformational change. Diffractive optical networks offer a promising solution for high-speed, low-power generative models.
Jumin Qiu +5 more
doaj +1 more source
SFCWGAN-BiTCN with Sequential Features for Malware Detection
Applied Sciences, 2023 In the field of adversarial attacks, the generative adversarial network (GAN) has shown better performance. There have been few studies applying it to malware sample supplementation, due to the complexity of handling discrete data.
Bona Xuan, Jin Li, Yafei Song
doaj +1 more source
Generative Adversarial Networks for Creating Synthetic Nucleic Acid Sequences of Cat Genome [PDF]
, 2022 Debapriya Hazra +2 more
openalex +1 more source
Information Transmission Strategies for Self‐Organized Robotic Aggregation
Advanced Robotics Research, EarlyView.In this review, we discuss how information transmission influences the neighbor‐based self‐organized aggregation of swarm robots. We focus specifically on local interactions regarding information transfer and categorize previous studies based on the functions of the information exchanged.
Shu Leng +5 more
wiley +1 more source
Dose distribution prediction for head-and-neck cancer radiotherapy using a generative adversarial network: influence of input data [PDF]
, 2023 Xiaojin Gu +4 more
openalex +1 more source