Results 31 to 40 of about 2,911,065 (235)

Survey on Research Progress of Generating Adversarial Networks

Jisuanji kexue yu tansuo, 2020
Since the birth of generative adversarial networks (GANs), the research on it has become a hot spot in the field of machine learning. It uses the mechanism of adversarial learning to train model solving the problem that the generation algorithm cannot ...
WU Shaoqian, LI Ximing
doaj   +1 more source

Survey on Generative Adversarial Behavior in Artificial Neural Tasks

Iraqi Journal for Computer Science and Mathematics, 2022
GANs (generative opposing networks) are a technique for learning deep representations in the absence of a large amount of annotated training data. This is accomplished through the use of a competitive technique that employs two networks to generate ...
Roheen Qamar, Naomi Bajao, Iswanto Suwarno, Fareed Ahmed Jokhio   +3 more
doaj   +1 more source

Improving Generative Adversarial Networks with Image Quality Assessment

, 2021
The research to find new ways to improve Generative Adversarial Networks (GANs) and ways to evaluate the data they produce is quite active. However, approaches to directly using those evaluation steps to improve Generative Adversarial Networks are quite ...
Perkins-Ollila, Justin W.
core   +5 more sources

A Survey of Image Synthesis and Editing with Generative Adversarial Networks

Tsinghua Science and Technology, 2017
This paper presents a survey of image synthesis and editing with Generative Adversarial Networks (GANs). GANs consist of two deep networks, a generator and a discriminator, which are trained in a competitive way. Due to the power of deep networks and the
Xian Wu, Kun Xu, Peter Hall
doaj   +1 more source

Attention-Aware Generative Adversarial Networks (ATA-GANs) [PDF]

2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), 2018
In this work, we present a novel approach for training Generative Adversarial Networks (GANs). Using the attention maps produced by a Teacher- Network we are able to improve the quality of the generated images as well as perform weakly object localization on the generated images.
Dimitris Kastaniotis, Ioanna Ntinou, Dimitrios Tsourounis, George Economou, Spiros Fotopoulos   +4 more
openaire   +2 more sources

House-GAN++: Generative Adversarial Layout Refinement Networks

CoRR, 2021
This paper proposes a novel generative adversarial layout refinement network for automated floorplan generation. Our architecture is an integration of a graph-constrained relational GAN and a conditional GAN, where a previously generated layout becomes the next input constraint, enabling iterative refinement.
Nelson Nauata, Sepidehsadat Hosseini, Kai-Hung Chang, Hang Chu, Chin-Yi Cheng, Yasutaka Furukawa   +5 more
openaire   +3 more sources

Creating Images with Stable Diffusion and Generative Adversarial Networks [PDF]

International Journal of Telecommunications
In this study, Generative Adversarial Networks (GANs) and Stable Diffusion represent two powerful methodologies in the field of generative models, with applications across image generation, creative design, and beyond. GANs consist of two neural networks,
mohamed sadek, A.Y. Hassan, Tamer O.Diab, Ahmed Abdelhafeez   +3 more
doaj   +1 more source

Generating Chest X-Ray Progression of Pneumonia Using Conditional Cycle Generative Adversarial Networks

IEEE Access, 2023
Pneumonia is an inflammation of the lungs caused by pathogens or autoimmune diseases, with about 450 million patients worldwide each year. Chest X–ray analysis is the most common radiographic method used to diagnose pneumonia, and advances in deep
Yeongbong Jin, Woojin Chang, Bonggyun Ko
doaj   +1 more source

GENERATIVE ADVERSARIAL NETWORKS (GAN)

This paper presents a comprehensive study on Generative Adversarial Networks (GANs), a powerful deep learning technique for generating realistic synthetic data. The work focuses on understanding the core architecture of GANs, which consists of two competing neural networks—the generator and the discriminator—trained through an adversarial learning ...
Anjana Raju, Shamas P M, Sheena K M
openaire   +4 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