Results 61 to 70 of about 27,491 (265)
Generative Adversarial Networks: An Overview [PDF]
IEEE Signal Processing Magazine, 2018 Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image ...
Antonia Creswell +5 more
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Hyperbolic Generative Adversarial Network
CoRR, 2021 Recently, Hyperbolic Spaces in the context of Non-Euclidean Deep Learning have gained popularity because of their ability to represent hierarchical data. We propose that it is possible to take advantage of the hierarchical characteristic present in the images by using hyperbolic neural networks in a GAN architecture.
Diego Lazcano +2 more
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Continual Learning for Multimodal Data Fusion of a Soft Gripper
Advanced Robotics Research, EarlyView.Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
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
Advanced Robotics Research, EarlyView.This work presents a state‐adaptive Koopman linear quadratic regulator framework for real‐time manipulation of a deformable swab tool in robotic environmental sampling. By combining Koopman linearization, tactile sensing, and centroid‐based force regulation, the system maintains stable contact forces and high coverage across flat and inclined surfaces.
Siavash Mahmoudi +2 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
Quantum generative adversarial networks [PDF]
Physical Review A, 2018 10 pages, 8 ...
Pierre-Luc Dallaire-Demers +1 more
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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
Applied Sciences, 2023 This paper introduces a novel and robust data-driven algorithm designed for Aircraft Trajectory Prediction (ATP). The approach employs a Neural Network architecture to predict future aircraft trajectories, utilizing input variables such as latitude ...
Seyed Mohammad Hashemi +3 more
doaj +1 more source
Slimmable Generative Adversarial Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Generative adversarial networks (GANs) have achieved remarkable progress in recent years, but the continuously growing scale of models make them challenging to deploy widely in practical applications. In particular, for real-time generation tasks, different devices require generators of different sizes due to varying computing power.
Liang Hou +5 more
openaire +2 more sources