Results 51 to 60 of about 219,974 (297)

Improved Wasserstein conditional generative adversarial network speech enhancement

EURASIP Journal on Wireless Communications and Networking, 2018
The speech enhancement based on the generative adversarial network has achieved excellent results with large quantities of data, but performance in the low-data regime and tasks like unseen data learning still lag behind.
Shan Qin, Ting Jiang
doaj   +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

Study of image reconstruction efficiency in a single-pixel imaging method using generative adversarial networks

Компьютерная оптика
Single-pixel imaging is a promising image acquisition method that provides an alternative to traditional imaging methods using multi-pixel matrices. However, algorithmic image reconstruction from measurements of a single-pixel camera is a non-trivial ...
D.V. Babukhin, A.A. Reutov, D.V. Sych
doaj   +1 more source

An Enhanced AI-Based Network Intrusion Detection System Using Generative Adversarial Networks

IEEE Internet of Things Journal, 2023
As communication technology advances, various and heterogeneous data are communicated in distributed environments through network systems. Meanwhile, along with the development of communication technology, the attack surface has expanded, and concerns ...
Cheolhee Park, Jonghoon Lee, Youngsoo Kim, Jong-Geun Park, Hyunjin Kim, Dowon Hong   +5 more
semanticscholar   +1 more source

Self-Sparse Generative Adversarial Networks

CAAI Artificial Intelligence Research, 2022
Generative Adversarial Networks (GANs) are an unsupervised generative model that learns data distribution through adversarial training. However, recent experiments indicated that GANs are difficult to train due to the requirement of optimization in the high dimensional parameter space and the zero gradient problem.
Wenliang Qian, Yang Xu, Wangmeng Zuo, Hui Li   +3 more
openaire   +3 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

Text Generation Based on Generative Adversarial Nets with Latent Variable

, 2018
In this paper, we propose a model using generative adversarial net (GAN) to generate realistic text. Instead of using standard GAN, we combine variational autoencoder (VAE) with generative adversarial net. The use of high-level latent random variables is
Qin, Zengchang, Wan, Tao, Wang, Heng
core   +1 more source

Entangling Quantum Generative Adversarial Networks

Physical Review Letters, 2022
Generative adversarial networks (GANs) are one of the most widely adopted semisupervised and unsupervised machine learning methods for high-definition image, video, and audio generation. In this work, we propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN) that overcomes some limitations of ...
Murphy Yuezhen Niu, Alexander Zlokapa, Michael Broughton, Sergio Boixo, Masoud Mohseni, Vadim Smelyanskyi, Hartmut Neven   +6 more
openaire   +4 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

ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network [PDF]

IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020
Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is a perceptual-driven approach for single image super-resolution that is able to produce photorealistic images.
Nathanaël Carraz Rakotonirina, Andry Rasoanaivo   +1 more
semanticscholar   +1 more source