Generative Adversarial Networks and Image Synthesis

TopicLeading institutions, researchers & key papers

This cluster of papers focuses on the application of Generative Adversarial Networks (GANs) in image processing, including image synthesis, style transfer, representation learning, and unsupervised learning. The papers cover various techniques such as image inpainting, texture synthesis, and conditional generative models using deep learning and neural networks.

Works

IDs:OpenAlex

How has Generative Adversarial Networks and Image Synthesis's publication output changed over time?

ScholarIQpublication output · 2000–2023

Output grew0% over the shown period — from 1 works in 2000 to 1 in 2023.

20002014201720182019202020212023

What are the most-cited papers on Generative Adversarial Networks and Image Synthesis?

ScholarIQmost cited works

Image inpainting

Marcelo Bertalmı́o, Guillermo Sapiro, V. Caselles, Coloma Ballester

20003,594 CitationsOPEN ACCESS

Score-Based Generative Modeling through Stochastic Differential Equations

Yang Song, Jascha Sohl‐Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole

arXiv (Cornell University). 20201,282 CitationsOPEN ACCESS

Temporal Generative Adversarial Nets with Singular Value Clipping

Masaki Saito, Eiichi Matsumoto, Shunta Saito

2017416 Citations

CogView: Mastering Text-to-Image Generation via Transformers

Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, Jie Tang

arXiv (Cornell University). 2021383 CitationsOPEN ACCESS

GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks

Christopher Bowles, Liang Chen, Ricardo Guerrero, Paul Bentley, Roger N. Gunn, Alexander Hammers, David Alexander Dickie, María del C. Valdés Hernández, Joanna M. Wardlaw, Daniel Rueckert

arXiv (Cornell University). 2018361 CitationsOPEN ACCESS