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The Generative Adversarial Random Neural Network

, 2021
Part 13: Machine LearningInternational audienceGenerative Adversarial Networks (GANs) have been proposed as a method to generate multiple replicas from an original version combining a Discriminator and a Generator. The main applications of GANs have been
Will Serrano, Serrano, Will
core   +1 more source

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

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

Enhanced Face Image Super-Resolution Using Generative Adversarial Network

, 2022
We proposed an Enhanced Face Image Generative Adversarial Network (EFGAN). Single image super-resolution (SISR) using a convolutional is often a problem in enhancing more refined texture upscaling factors. Our approach focused on mean square error (MSE),
Hardiansyah, Bagus, Hartono, Elvianto Dwi   +1 more
core   +1 more source

Annealed Generative Adversarial Networks

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

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

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

Unrolled Generative Adversarial Networks

CoRR, 2016
We introduce a method to stabilize Generative Adversarial Networks (GANs) by defining the generator objective with respect to an unrolled optimization of the discriminator. This allows training to be adjusted between using the optimal discriminator in the generator's objective, which is ideal but infeasible in practice, and using the current value of ...
Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein   +3 more
openaire   +3 more sources

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