Results 41 to 50 of about 27,491 (265)

Generative Adversarial Network-Based Edge-Preserving Superresolution Reconstruction of Infrared Images

International Journal of Digital Multimedia Broadcasting, 2021
The convolutional neural network has achieved good results in the superresolution reconstruction of single-frame images. However, due to the shortcomings of infrared images such as lack of details, poor contrast, and blurred edges, superresolution ...
Yuqing Zhao, Guangyuan Fu, Hongqiao Wang, Shaolei Zhang, Min Yue   +4 more
doaj   +1 more source

Triangle Generative Adversarial Networks

CoRR, 2017
A Triangle Generative Adversarial Network ($Δ$-GAN) is developed for semi-supervised cross-domain joint distribution matching, where the training data consists of samples from each domain, and supervision of domain correspondence is provided by only a few paired samples.
Zhe Gan, Liqun Chen 0001, Weiyao Wang 0002, Yunchen Pu, Yizhe Zhang 0002, Hao Liu 0015, Chunyuan Li, Lawrence Carin   +7 more
openaire   +3 more sources

Modular Generative Adversarial Networks [PDF]

, 2018
Existing methods for multi-domain image-to-image translation (or generation) attempt to directly map an input image (or a random vector) to an image in one of the output domains. However, most existing methods have limited scalability and robustness, since they require building independent models for each pair of domains in question.
Bo Zhao 0032, Bo Chang 0002, Zequn Jie, Leonid Sigal   +3 more
openaire   +2 more sources

Graphical Generative Adversarial Networks

CoRR, 2018
We propose Graphical Generative Adversarial Networks (Graphical-GAN) to model structured data. Graphical-GAN conjoins the power of Bayesian networks on compactly representing the dependency structures among random variables and that of generative adversarial networks on learning expressive dependency functions.
Chongxuan Li, Max Welling, Jun Zhu 0001, Bo Zhang 0010   +3 more
openaire   +3 more sources

Stacked Generative Adversarial Networks [PDF]

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
CVPR 2017, camera-ready ...
Xun Huang 0002, Yixuan Li 0001, Omid Poursaeed, John E. Hopcroft, Serge J. Belongie   +4 more
openaire   +2 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

Coupled Generative Adversarial Networks

CoRR, 2016
We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images. In contrast to the existing approaches, which require tuples of corresponding images in different domains in the training set, CoGAN can learn a joint distribution without any tuple of corresponding images.
Ming-Yu Liu 0001, Oncel Tuzel
openaire   +3 more sources

Evolutionary Generative Adversarial Networks [PDF]

IEEE Transactions on Evolutionary Computation, 2019
14 pages, 8 ...
Chaoyue Wang, Chang Xu 0002, Xin Yao 0001, Dacheng Tao   +3 more
openaire   +2 more sources

Functionalizing Micro‐to‐Mesoscopic Electrode Architectures for Regulating Electron Transfer Behaviors in Electrocatalysis

Advanced Functional Materials, EarlyView.
A systematic review is conducted to assess the influence of electrode architecture across micro‐ to mesoscopic length scales on electron‐transfer pathways in electrocatalysis. We discuss the structure‐activity relationships in electrocatalytic applications, including resource recovery and environmental remediation, and provide cost‐effective, efficient
Manshu Zhao, Jiaojiao Zhang, Shideng Yuan, Xinmeng Yu, Keming Wan, Xiaoguang Duan, Zhining Wang   +6 more
wiley   +1 more source

Annealed Generative Adversarial Networks

CoRR, 2017
9 pages, 6 ...
Arash Mehrjou, Bernhard Schölkopf, Saeed Saremi   +2 more
openaire   +2 more sources