Results 51 to 60 of about 2,911,065 (235)
Generative Target Tracking Method with Improved Generative Adversarial Network
IET Circuits, Devices and Systems, 2023 Multitarget tracking is prone to target loss, identity exchange, and jumping problems in the context of complex background, target occlusion, target scale, and pose transformation.
Yongping Yang, Hongshun Chen
doaj +1 more source
Generative Adversarial Network (GAN) to Generate Realistic Images
International Journal for Research in Applied Science and Engineering Technology, 2023 Abstract: Generative Adversarial Networks (GANs) have rapidly become a focal point of research due to their ability to generate realistic images. First introduced in 2014, GANs have been applied in a multitude of fields such as computer vision and natural language processing, yielding impressive results.
Sahil Lamba +3 more
openaire +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
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
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
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 +2 more
doaj +1 more source
Advanced Robotics Research, EarlyView.This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
CoRR, 2018 Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications, and many GAN variants have emerged with improvements in sample quality and training stability. However, they have not been well visualized or understood. How does a GAN represent our visual world internally?
David Bau +6 more
openaire +4 more sources
Generative Adversarial Networks (GANs Survey): Challenges, Solutions, and Future Directions [PDF]
, 2023 Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major
Cao, Jiannong, Saxena, Divya
core
Solid Harmonic Wavelet Bispectrum for Image Analysis
Advanced Science, EarlyView.The Solid Harmonic Wavelet Bispectrum (SHWB), a rotation‐ and translation‐invariant descriptor that captures higher‐order (phase) correlations in signals, is introduced. Combining wavelet scattering, bispectral analysis, and group theory, SHWB achieves interpretable, data‐efficient representations and demonstrates competitive performance across texture,
Alex Brown +3 more
wiley +1 more source