Adversarial Network

Adversarial networks, particularly Generative Adversarial Networks (GANs), are powerful machine learning models designed to generate realistic data by pitting two neural networks against each other: a generator creating synthetic data and a discriminator evaluating its authenticity. Current research focuses on extending GANs to various data types, including graphs representing complex relationships (e.g., brain connectivity, wind farm networks, microbial interactions), leveraging graph convolutional networks to capture structural information. This approach finds applications in diverse fields, from disease prediction using microbiome data and improving medical image analysis to generating realistic scenarios for power grid simulations and enhancing 3D texture synthesis. The resulting improvements in data generation and analysis have significant implications for various scientific domains and practical applications.

Papers

July 31, 2024

UMMAN: Unsupervised Multi-graph Merge Adversarial Network for Disease Prediction Based on Intestinal Flora
Dingkun Liu, Hongjie Zhou, Yilu Qu, Huimei Zhang, Yongdong Xu
Disease Prediction Microbiome Datasets Microbiome Research Adversarial Network

September 16, 2023

BG-GAN: Generative AI Enables Representing Brain Structure-Function Connections for Alzheimer's Disease
Changhong Jing, Chen Ding, Shuqiang Wang
Generative Adversarial Network Generative AI Alzheimer'S Disease Brain Function Structure Function Adversarial Network

December 19, 2022

Wind Power Scenario Generation Using Graph Convolutional Generative Adversarial Network
Young-ho Cho, Shaohui Liu, Duehee Lee, Hao Zhu
GAN Model Wind Power Adversarial Network Wind Power Scenario

December 14, 2022

Population Template-Based Brain Graph Augmentation for Improving One-Shot Learning Classification
Oben Özgür, Arwa Rekik, Islem Rekik
Graph Data Structural Connectome Na\"ive Population Brain Template Adversarial Network

June 17, 2022

Texture Generation Using A Graph Generative Adversarial Network And Differentiable Rendering
Dharma KC, Clayton T. Morrison, Bradley Walls
Generative Adversarial Network Conditional Generative Texture Synthesis Closed Form Differentiable Expression Texture Generation Adversarial Network

November 17, 2021

Generating Unrestricted 3D Adversarial Point Clouds
Xuelong Dai, Yanjie Li, Hua Dai, Bin Xiao
Adversarial Point Cloud Unrestricted Adversarial Adversarial Network

Adversarial Network

Papers

UMMAN: Unsupervised Multi-graph Merge Adversarial Network for Disease Prediction Based on Intestinal Flora

BG-GAN: Generative AI Enables Representing Brain Structure-Function Connections for Alzheimer's Disease

Wind Power Scenario Generation Using Graph Convolutional Generative Adversarial Network

Population Template-Based Brain Graph Augmentation for Improving One-Shot Learning Classification

Texture Generation Using A Graph Generative Adversarial Network And Differentiable Rendering

Generating Unrestricted 3D Adversarial Point Clouds