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
3DGAUnet: 3D generative adversarial networks with a 3D U-Net based generator to achieve the accurate and effective synthesis of clinical tumor image data for pancreatic cancer
Yu Shi, Hannah Tang, Michael Baine, Michael A. Hollingsworth, Huijing Du, Dandan Zheng, Chi Zhang, Hongfeng Yu
L-WaveBlock: A Novel Feature Extractor Leveraging Wavelets for Generative Adversarial Networks
Mirat Shah, Vansh Jain, Anmol Chokshi, Guruprasad Parasnis, Pramod Bide
Robust Retraining-free GAN Fingerprinting via Personalized Normalization
Jianwei Fei, Zhihua Xia, Benedetta Tondi, Mauro Barni
Social Media Bot Detection using Dropout-GAN
Anant Shukla, Martin Jurecek, Mark Stamp
3D EAGAN: 3D edge-aware attention generative adversarial network for prostate segmentation in transrectal ultrasound images
Mengqing Liu, Xiao Shao, Liping Jiang, Kaizhi Wu
Improving the Effectiveness of Deep Generative Data
Ruyu Wang, Sabrina Schmedding, Marco F. Huber
MeVGAN: GAN-based Plugin Model for Video Generation with Applications in Colonoscopy
Łukasz Struski, Tomasz Urbańczyk, Krzysztof Bucki, Bartłomiej Cupiał, Aneta Kaczyńska, Przemysław Spurek, Jacek Tabor
Unsupervised Video Summarization via Iterative Training and Simplified GAN
Hanqing Li, Diego Klabjan, Jean Utke
Multi-Resolution Diffusion for Privacy-Sensitive Recommender Systems
Derek Lilienthal, Paul Mello, Magdalini Eirinaki, Stas Tiomkin
Preserving Privacy in GANs Against Membership Inference Attack
Mohammadhadi Shateri, Francisco Messina, Fabrice Labeau, Pablo Piantanida
A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection
Wenxin Wang, Zhuo-Xu Cui, Guanxun Cheng, Chentao Cao, Xi Xu, Ziwei Liu, Haifeng Wang, Yulong Qi, Dong Liang, Yanjie Zhu
Flexible Multi-Generator Model with Fused Spatiotemporal Graph for Trajectory Prediction
Peiyuan Zhu, Fengxia Han, Hao Deng
A Strictly Bounded Deep Network for Unpaired Cyclic Translation of Medical Images
Swati Rai, Jignesh S. Bhatt, Sarat Kumar Patra
MTS-DVGAN: Anomaly Detection in Cyber-Physical Systems using a Dual Variational Generative Adversarial Network
Haili Sun, Yan Huang, Lansheng Han, Cai Fu, Hongle Liu, Xiang Long