Results 61 to 70 of about 222,297 (252)

Spatial evolutionary generative adversarial networks [PDF]

Proceedings of the Genetic and Evolutionary Computation Conference, 2019
Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse. These pathologies mainly arise from a lack of diversity in their adversarial interactions. Evolutionary generative adversarial networks apply the principles of evolutionary computation to mitigate these problems.
Toutouh, Jamal, Hemberg, Erik, O'Reilly, Una-May   +2 more
openaire   +3 more sources

Lung image segmentation via generative adversarial networks

Frontiers in Physiology
IntroductionLung image segmentation plays an important role in computer-aid pulmonary disease diagnosis and treatment.MethodsThis paper explores the lung CT image segmentation method by generative adversarial networks.
Jiaxin Cai, Hongfeng Zhu, Siyu Liu, Yang Qi, Rongshang Chen   +4 more
doaj   +1 more source

Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Ehécatl

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, Seyed Ali Hashemi, Ruxandra Mihaela Botez, Georges Ghazi   +3 more
doaj   +1 more source

Attentively Conditioned Generative Adversarial Network for Semantic Segmentation

IEEE Access, 2020
Generative Adversarial Network has proven to produce state-of-the-art results by framing a generative modeling task into a supervised learning problem. In this paper, we propose Attentively Conditioned Generative Adversarial Network (ACGAN) for semantic ...
Ariyo Oluwasanmi, Muhammad Umar Aftab, Akeem Shokanbi, Jehoiada Jackson, Bulbula Kumeda, Zhiquang Qin   +5 more
doaj   +1 more source

PAMSGAN: Pyramid Attention Mechanism-Oriented Symmetry Generative Adversarial Network for Motion Image Deblurring

IEEE Access, 2021
Motion blur is a common problem in optical imaging, which is caused by the relative displacement between the subject and the camera in the exposure process of the camera.
Zhenfeng Zhang
doaj   +1 more source

Adversarial Spatio-Temporal Learning for Video Deblurring

, 2018
Camera shake or target movement often leads to undesired blur effects in videos captured by a hand-held camera. Despite significant efforts having been devoted to video-deblur research, two major challenges remain: 1) how to model the spatio-temporal ...
Li, Hongdong, Liu, Wei, Luo, Wenhan, Ma, Lin, Zhang, Kaihao, Zhong, Yiran   +5 more
core   +1 more source

Continuous Sign Language Recognition through a Context-Aware Generative Adversarial Network

Italian National Conference on Sensors, 2021
Continuous sign language recognition is a weakly supervised task dealing with the identification of continuous sign gestures from video sequences, without any prior knowledge about the temporal boundaries between consecutive signs.
Ilias Papastratis, K. Dimitropoulos, P. Daras   +2 more
semanticscholar   +1 more source

Tomographic reconstruction with a generative adversarial network [PDF]

Journal of Synchrotron Radiation, 2020
This paper presents a deep learning algorithm for tomographic reconstruction (GANrec). The algorithm uses a generative adversarial network (GAN) to solve the inverse of the Radon transform directly. It works for independent sinograms without additional training steps.
Yang, Xiaogang, Kahnt, Maik, Brückner, Dennis, Schropp, Andreas, Fam, Yakub, Becher, Johannes, Grunwaldt, Jan-Dierk, Sheppard, Thomas L., Schroer, Christian G.   +8 more
openaire   +5 more sources

Text2Action: Generative Adversarial Synthesis from Language to Action

, 2017
In this paper, we propose a generative model which learns the relationship between language and human action in order to generate a human action sequence given a sentence describing human behavior.
Ahn, Hyemin, Choi, Yunho, Ha, Timothy, Oh, Songhwai, Yoo, Hwiyeon   +4 more
core   +1 more source

Generative Adversarial Networks in finance: an overview

, 2021
Modelling in finance is a challenging task: the data often has complex statistical properties and its inner workings are largely unknown. Deep learning algorithms are making progress in the field of data-driven modelling, but the lack of sufficient data to train these models is currently holding back several new applications.
Eckerli, Florian, Osterrieder, Joerg
openaire   +3 more sources