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

January 9, 2023

Multiscale Metamorphic VAE for 3D Brain MRI Synthesis
Jaivardhan Kapoor, Jakob H. Macke, Christian F. Baumgartner
Generative Adversarial Network Generative Modeling Variational Autoencoder 3D Brain Brain MRI Synthesis

January 7, 2023

January 5, 2023

DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection
Caroline Strickland, Chandrika Saha, Muhammad Zakar, Sareh Nejad, Noshin Tasnim, Daniel Lizotte, Anwar Haque
Generative Adversarial Network Intrusion Detection System Hybrid Approach Binary Code Multi Class Anomaly Detection Advanced Persistent Threat

January 3, 2023

January 2, 2023

Learning Invariance from Generated Variance for Unsupervised Person Re-identification
Hao Chen, Yaohui Wang, Benoit Lagadec, Antitza Dantcheva, Francois Bremond
Contrastive Learning Generative Adversarial Network Person Re Identification Variance Information Conditional Invariance

January 1, 2023

SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation
Kunal Chaturvedi, Ali Braytee, Jun Li, Mukesh Prasad
Generative Adversarial Network Self Supervised Learning Object Segmentation Self Supervised Task Visual Feedback Foreground Instance Segmentation

December 30, 2022

December 29, 2022

Discriminator-Cooperated Feature Map Distillation for GAN Compression
Tie Hu, Mingbao Lin, Lizhou You, Fei Chao, Rongrong Ji
Generative Adversarial Network Map Distillation GAN Compression

December 28, 2022

December 26, 2022

Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images
Weizhi Du, Harvery Tian
Generative Adversarial Network Transformer Based Super Resolution Medical Image C Gan Super Resolution Reconstruction

December 25, 2022

Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image
Samer Kais Jameel, Sezgin Aydin, Nebras H. Ghaeb, Jafar Majidpour, Tarik A. Rashid, Sinan Q. Salih, P. S. JosephNg
Generative Adversarial Network Medical Image Generative Adversarial Conditional Generative Adversarial Network Corneal Image

December 24, 2022

HandsOff: Labeled Dataset Generation With No Additional Human Annotations
Austin Xu, Mariya I. Vasileva, Achal Dave, Arjun Seshadri
Generative Adversarial Network Synthetic Data Synthetic Image Human Annotation GAN Inversion Dataset Generation

December 22, 2022

Jamdani Motif Generation using Conditional GAN
MD Tanvir Rouf Shawon, Raihan Tanvir, Humaira Ferdous Shifa, Susmoy Kar, Mohammad Imrul Jubair
Generative Adversarial Network AI Generated Image Conditional GAN Jamdani Motif

Generative Adversarial Network

Papers

Multiscale Metamorphic VAE for 3D Brain MRI Synthesis

Deep Learning for Mean Field Games with non-separable Hamiltonians

GAN-Based Content Generation of Maps for Strategy Games

DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection

Neural SDEs for Conditional Time Series Generation and the Signature-Wasserstein-1 metric

On the causality-preservation capabilities of generative modelling

Learning Invariance from Generated Variance for Unsupervised Person Re-identification

SS-CPGAN: Self-Supervised Cut-and-Pasting Generative Adversarial Network for Object Segmentation

GANExplainer: GAN-based Graph Neural Networks Explainer

Modified Query Expansion Through Generative Adversarial Networks for Information Extraction in E-Commerce

Discriminator-Cooperated Feature Map Distillation for GAN Compression

StyleID: Identity Disentanglement for Anonymizing Faces

Distribution Estimation of Contaminated Data via DNN-based MoM-GANs

Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharing

Knowledge-Guided Data-Centric AI in Healthcare: Progress, Shortcomings, and Future Directions

Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classification

Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images

Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image

HandsOff: Labeled Dataset Generation With No Additional Human Annotations

Jamdani Motif Generation using Conditional GAN