Generative Adversarial Network
Generative Adversarial Networks (GANs) are a class of deep learning models designed to generate new data instances that resemble a training dataset. Current research focuses on improving GAN training stability, enhancing the quality and diversity of generated data, and applying GANs to diverse fields like medical imaging, drug discovery, and time series analysis, often incorporating techniques like contrastive learning and disentangled representation learning to improve model performance and interpretability. The ability of GANs to synthesize realistic data addresses critical limitations in data availability and annotation costs across numerous scientific disciplines and practical applications, leading to advancements in areas ranging from medical diagnosis to robotic control.
Papers
A Deep-Learning Method Using Auto-encoder and Generative Adversarial Network for Anomaly Detection on Ancient Stone Stele Surfaces
Yikun Liu, Yuning Wang, Cheng Liu
Domain Adaptive Person Search via GAN-based Scene Synthesis for Cross-scene Videos
Huibing Wang, Tianxiang Cui, Mingze Yao, Huijuan Pang, Yushan Du
Generation of Realistic Synthetic Raw Radar Data for Automated Driving Applications using Generative Adversarial Networks
Eduardo C. Fidelis, Fabio Reway, Herick Y. S. Ribeiro, Pietro L. Campos, Werner Huber, Christian Icking, Lester A. Faria, Torsten Schön
Painterly Image Harmonization using Diffusion Model
Lingxiao Lu, Jiangtong Li, Junyan Cao, Li Niu, Liqing Zhang
Large-Scale Public Data Improves Differentially Private Image Generation Quality
Ruihan Wu, Chuan Guo, Kamalika Chaudhuri
Graph Contrastive Learning with Generative Adversarial Network
Cheng Wu, Chaokun Wang, Jingcao Xu, Ziyang Liu, Kai Zheng, Xiaowei Wang, Yang Song, Kun Gai
Generative adversarial networks with physical sound field priors
Xenofon Karakonstantis, Efren Fernandez-Grande
Synthetic Skull CT Generation with Generative Adversarial Networks to Train Deep Learning Models for Clinical Transcranial Ultrasound
Kasra Naftchi-Ardebili, Karanpartap Singh, Reza Pourabolghasem, Pejman Ghanouni, Gerald R. Popelka, Kim Butts Pauly
A multiscale and multicriteria Generative Adversarial Network to synthesize 1-dimensional turbulent fields
Carlos Granero-Belinchon, Manuel Cabeza Gallucci
Don't be so negative! Score-based Generative Modeling with Oracle-assisted Guidance
Saeid Naderiparizi, Xiaoxuan Liang, Berend Zwartsenberg, Frank Wood