Results 41 to 50 of about 134,465 (305)

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

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

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

Generative Adversarial Networks: An Overview [PDF]

IEEE Signal Processing Magazine, 2018
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image ...
Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, Anil A. Bharath   +5 more
openaire   +4 more sources

Generative Adversarial Networks: A Primer for Radiologists

RadioGraphics, 2021
Artificial intelligence techniques involving the use of artificial neural networks-that is, deep learning techniques-are expected to have a major effect on radiology. Some of the most exciting applications of deep learning in radiology make use of generative adversarial networks (GANs).
Jelmer M. Wolterink, Anirban Mukhopadhyay, Tim Leiner, Thomas J. Vogl, Andreas M. Bucher, Ivana Išgum   +5 more
openaire   +5 more sources

Adversarial Spatio-Temporal Learning for Video Deblurring

, 2018
Camera shake or target movement often leads to undesired blur effects in videos captured by a hand-held camera. Despite significant efforts having been devoted to video-deblur research, two major challenges remain: 1) how to model the spatio-temporal ...
Li, Hongdong, Liu, Wei, Luo, Wenhan, Ma, Lin, Zhang, Kaihao, Zhong, Yiran   +5 more
core   +1 more source

Meniscus Pixel Printing for Contact‐Lens Vision Sensing and Robotic Control

Advanced Functional Materials, EarlyView.
A visual‐sensing contact lens is enabled by meniscus pixel printing (MPP), which rapidly patterns a 200 µm perovskite photodetector pixel in 1 s without masks, vacuum processing, or bulky equipment. A deep‐learning‐based super‐resolution reconstructs sparse on‐lens signals into 80 × 80 high‐resolution visual information, while AI‐driven eye‐tracking ...
Byung‐Hoon Gong, Dohyean Kim, Jiyun Jeong, Taekyeong Kim, Soon‐sung Kwon, Junyoung Seo, Il Jeon, Im Doo Jung   +7 more
wiley   +1 more source

Attentively Conditioned Generative Adversarial Network for Semantic Segmentation

IEEE Access, 2020
Generative Adversarial Network has proven to produce state-of-the-art results by framing a generative modeling task into a supervised learning problem. In this paper, we propose Attentively Conditioned Generative Adversarial Network (ACGAN) for semantic ...
Ariyo Oluwasanmi, Muhammad Umar Aftab, Akeem Shokanbi, Jehoiada Jackson, Bulbula Kumeda, Zhiquang Qin   +5 more
doaj   +1 more source

Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Ehécatl

Applied Sciences, 2023
This paper introduces a novel and robust data-driven algorithm designed for Aircraft Trajectory Prediction (ATP). The approach employs a Neural Network architecture to predict future aircraft trajectories, utilizing input variables such as latitude ...
Seyed Mohammad Hashemi, Seyed Ali Hashemi, Ruxandra Mihaela Botez, Georges Ghazi   +3 more
doaj   +1 more source