Results 51 to 60 of about 4,829,196 (302)

Extended Semi-Supervised Learning Generative Adversarial Network [PDF]

An extended semi-supervised learning (ESSL) generative adversarial network (GAN) including metrics for evaluating training performance and a method for generating an esti­mated label vector y by the extended semi-supervised learn­ing (ESSL) generative ...
Scrofani, James, Tummala, Murali, Hahn, Andrew W.   +2 more
core   +2 more sources

Generative Adversarial Optical Networks Using Diffractive Layers for Digit and Action Generation

Photonics
Within the traditional electronic neural network framework, Generative Adversarial Networks (GANs) have achieved extensive applications across multiple domains, including image synthesis, style transfer and data augmentation.
Pei Hu, Tengyu Cui, Yuanyuan Zhang, Shuai Feng   +3 more
doaj   +1 more source

Implementasi Steganografi Gambar Menggunakan Algoritma Generative Adversarial Network

SINTECH (Science and Information Technology) Journal, 2023
In the era of information technology, it is very important to protect data and information so that irresponsible parties do not misuse it. One technique for securing data is steganography. Steganography is a technique of hiding messages in a medium.
Khairunnisak Khairunnisak, Gilang Miftakhul Fahmi, Didit Suhartono   +2 more
doaj   +1 more source

Generative Adversarial Mapping Networks

CoRR, 2017
9 pages, 7 ...
Jianbo Guo, Guangxiang Zhu, Jian Li
openaire   +3 more sources

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

On the "steerability" of generative adversarial networks

CoRR, 2019
An open secret in contemporary machine learning is that many models work beautifully on standard benchmarks but fail to generalize outside the lab. This has been attributed to biased training data, which provide poor coverage over real world events.
Ali Jahanian 0002, Lucy Chai, Phillip Isola   +2 more
openaire   +3 more sources

Deconstructing Generative Adversarial Networks [PDF]

IEEE Transactions on Information Theory, 2020
We deconstruct the performance of GANs into three components: 1. Formulation: we propose a perturbation view of the population target of GANs. Building on this interpretation, we show that GANs can be viewed as a generalization of the robust statistics framework, and propose a novel GAN architecture, termed as Cascade GANs, to provably recover ...
Banghua Zhu, Jiantao Jiao, David Tse
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   +3 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

Ensembles of Generative Adversarial Networks

CoRR, 2016
accepted NIPS 2016 Workshop on Adversarial ...
Yaxing Wang, Lichao Zhang 0001, Joost van de Weijer 0001   +2 more
openaire   +2 more sources