Results 311 to 320 of about 222,396 (351)

TTS-GAN: A Transformer-based Time-Series Generative Adversarial Network

Conference on Artificial Intelligence in Medicine in Europe, 2022
Signal measurements appearing in the form of time series are one of the most common types of data used in medical machine learning applications. However, such datasets are often small, making the training of deep neural network architectures ineffective.
Xiaomin Li, V. Metsis, Huan Wang, Anne H. H. Ngu   +3 more
semanticscholar   +1 more source

Remote Sensing Image Spatiotemporal Fusion Using a Generative Adversarial Network

IEEE Transactions on Geoscience and Remote Sensing, 2021
Due to technological limitations and budget constraints, spatiotemporal fusion is considered a promising way to deal with the tradeoff between the temporal and spatial resolutions of remote sensing images. Furthermore, the generative adversarial network (
Hongyan Zhang, Yiyao Song, Chang Han, L. Zhang   +3 more
semanticscholar   +1 more source

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

ECCV Workshops, 2018
The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts.
Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Y. Qiao, Xiaoou Tang   +8 more
semanticscholar   +1 more source

Infrared and Visible Image Fusion via Texture Conditional Generative Adversarial Network

IEEE transactions on circuits and systems for video technology (Print), 2021
This paper proposes an effective infrared and visible image fusion method based on a texture conditional generative adversarial network (TC-GAN). The constructed TC-GAN generates a combined texture map for capturing gradient changes in image fusion.
Yong Yang, Jiaxiang Liu, Shuying Huang, Weiguo Wan, Wenying Wen, Juwei Guan   +5 more
semanticscholar   +1 more source

FISS GAN: A Generative Adversarial Network for Foggy Image Semantic Segmentation

IEEE/CAA Journal of Automatica Sinica, 2021
Because pixel values of foggy images are irregularly higher than those of images captured in normal weather (clear images), it is difficult to extract and express their texture. No method has previously been developed to directly explore the relationship
Kunhua Liu, Zihao Ye, Hongyan Guo, Dongpu Cao, Long Chen, Feiyue Wang   +5 more
semanticscholar   +1 more source

SolarGAN: Multivariate Solar Data Imputation Using Generative Adversarial Network

IEEE Transactions on Sustainable Energy, 2021
Photovoltaic (PV) is receiving increasing attention due to its sustainability and low carbon footprint. However, the penetration level of PV is still relatively low because of its intermittency. This uncertainty can be handled by accurate PV forecasting,
W. Zhang, Yonghong Luo, Ying Zhang, D. Srinivasan   +3 more
semanticscholar   +1 more source

Underwater Image Enhancement Using a Multiscale Dense Generative Adversarial Network

IEEE Journal of Oceanic Engineering, 2020
Underwater image enhancement has received much attention in underwater vision research. However, raw underwater images easily suffer from color distortion, underexposure, and fuzz caused by the underwater scene.
Ye-cai Guo, Hanyu Li, Peixian Zhuang
semanticscholar   +1 more source

Generative Adversarial Network (GAN): a general review on different variants of GAN and applications

International Conference on Communication and Electronics Systems, 2021
Deep learning plays a very important role in the research area in the field of Artificial Intelligence (AI) and Machine Learning (ML) and many models have been developed based on GAN applications.
K. S, M. Durgadevi
semanticscholar   +1 more source

Generative adversarial network for road damage detection

Comput. Aided Civ. Infrastructure Eng., 2020
Machine learning can produce promising results when sufficient training data are available; however, infrastructure inspections typically do not provide sufficient training data for road damage.
Hiroya Maeda, Takehiro Kashiyama, Y. Sekimoto, Toshikazu Seto, Hiroshi Omata   +4 more
semanticscholar   +1 more source

Generative Adversarial Networks for Face Generation: A Survey

ACM Computing Surveys, 2022
Recently, generative adversarial networks (GANs) have progressed enormously, which makes them able to learn complex data distributions in particular faces. More and more efficient GAN architectures have been designed and proposed to learn the different variations of faces, such as cross pose, age, expression, and style.
Amina Kammoun, Rim Slama, Hedi Tabia, Tarek Ouni, Mohmed Abid   +4 more
openaire   +2 more sources