Results 51 to 60 of about 221,929 (334)
Building Footprint Generation Using Improved Generative Adversarial Networks [PDF]
, 2018 Building footprint information is an essential ingredient for 3-D reconstruction of urban models. The automatic generation of building footprints from satellite images presents a considerable challenge due to the complexity of building shapes.
Li, Qingyu, Shi, Yilei, Zhu, Xiao Xiang
core +2 more sources
Image Super-Resolution Reconstruction Algorithm Based on Generative Adversarial Networks [PDF]
Jisuanji gongcheng, 2021 The existing image Super-Resolution (SR) reconstruction algorithms have difficulty in network training and cause artifacts in the generated images.To address the problem,this paper proposes a SR reconstruction algorithm based on Generative Adversarial ...
JIANG Yuning, LI Jinhua, ZHAO Junli
doaj +1 more source
GAN and Chinese WordNet Based Text Summarization Technology [PDF]
Jisuanji kexue, 2022 Since the introduction of neural networks,text summarization techniques continue to attract the attention of resear-chers.Similarly,generative adversarial networks(GANs)can be used for text summarization because they can generate text features or learn ...
LIU Xiao-ying, WANG Huai, WU Jisiguleng
doaj +1 more source
Enhancing Underwater Imagery Using Generative Adversarial Networks [PDF]
IEEE International Conference on Robotics and Automation, 2018 Autonomous underwater vehicles (AUVs) rely on a variety of sensors - acoustic, inertial and visual - for intelligent decision making. Due to its non-intrusive, passive nature and high information content, vision is an attractive sensing modality ...
C. Fabbri, M. Islam, Junaed Sattar
semanticscholar +1 more source
Generate to Adapt: Aligning Domains Using Generative Adversarial Networks [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 Domain Adaptation is an actively researched problem in Computer Vision. In this work, we propose an approach that leverages unsupervised data to bring the source and target distributions closer in a learned joint feature space.
S. Sankaranarayanan +3 more
semanticscholar +1 more source
A quantum generative adversarial network for distributions [PDF]
Quantum Machine Intelligence, 2021 AbstractRecent advances in Quantum Computing have shown that, despite the absence of a fault-tolerant quantum computer so far, quantum techniques are providing exponential advantage over their classical counterparts. We develop a fully connected Quantum Generative Adversarial network and show how it can be applied in Mathematical Finance, with a ...
Amine Assouel +2 more
openaire +2 more sources
Recent Generative Adversarial Approach in Face Aging and Dataset Review
IEEE Access, 2022 Many studies have been conducted in the field of face aging, from approaches that use pure image-processing algorithms, to those that use generative adversarial networks.
Hady Pranoto +3 more
doaj +1 more source
A progressive growing of conditional generative adversarial networks model
Dianxin kexue, 2023 Progressive growing of generative adversarial networks (PGGAN) is an adversarial network model that can generate high-resolution images.However, when the categories of samples are unbalanced, or the categories of samples are too similar or too dissimilar,
Hui MA, Ruiqin WANG, Shuai YANG
doaj +2 more sources
Lung image segmentation via generative adversarial networks
Frontiers in PhysiologyIntroductionLung 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 +4 more
doaj +1 more source
Defending against and generating adversarial examples together with generative adversarial networks. [PDF]
Sci RepAlthough deep neural networks have achieved great success in many tasks, they encounter security threats and are often fooled by adversarial examples, which are created by making slight modifications to pixel values. To address these problems, a novel DG-GAN framework is proposed, integrating generator, encoder, and discriminator, to defend against and
Wang Y, Liao X, Cui W, Yang Y.
europepmc +4 more sources