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

July 16, 2023

July 14, 2023

Generative adversarial networks for data-scarce spectral applications
Juan José García-Esteban, Juan Carlos Cuevas, Jorge Bravo-Abad
Generative Adversarial Network Adversarial Learning Generative Question Wasserstein Generative Adversarial Network Wasserstein Gan Synthetic Spectrum Spectral Band

July 11, 2023

July 10, 2023

StyleGAN2-based Out-of-Distribution Detection for Medical Imaging
McKell Woodland, John Wood, Caleb O'Connor, Ankit B. Patel, Kristy K. Brock
Generative Adversarial Network Medical Imaging Distribution Data CT Image Non Contrast

July 9, 2023

A generative flow for conditional sampling via optimal transport
Jason Alfonso, Ricardo Baptista, Anupam Bhakta, Noam Gal, Alfin Hou, Isa Lyubimova, Daniel Pocklington, Josef Sajonz, Giulio Trigila, Ryan Tsai
Generative Model Generative Adversarial Network Optimal Transport Conditional Distribution Conditional Sampling Generative Flow Non Parametric Generative

July 7, 2023

Synthesizing Forestry Images Conditioned on Plant Phenotype Using a Generative Adversarial Network
Debasmita Pal, Arun Ross
Generative Adversarial Network Synthetic Image Tree Dataset Plant Phenotype

July 6, 2023

A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications
Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto, Simone Palazzo, Ulas Bagci, Concetto Spampinato
Generative Model Generative Adversarial Network Latent Space Deep Model Medical Application Synthetic Sample

July 5, 2023

July 4, 2023

Disentanglement in a GAN for Unconditional Speech Synthesis
Matthew Baas, Herman Kamper
Generative Adversarial Network Latent Space GAN Model Speech Synthesis Disentanglement Capability

July 1, 2023

June 29, 2023

June 26, 2023

A Simple and Effective Baseline for Attentional Generative Adversarial Networks
Mingyu Jin, Chong Zhang, Qinkai Yu, Haochen Xue, Xiaobo Jin, Xi Yang
Generative Adversarial Network GAN Model C Gan GAN Training Effective Baseline

Generative Adversarial Network

Papers

Self-Attention Based Generative Adversarial Networks For Unsupervised Video Summarization

Householder Projector for Unsupervised Latent Semantics Discovery

Generative adversarial networks for data-scarce spectral applications

Efficient 3D Articulated Human Generation with Layered Surface Volumes

Disentangled Contrastive Image Translation for Nighttime Surveillance

Diffusion idea exploration for art generation

Image Reconstruction using Enhanced Vision Transformer

StyleGAN2-based Out-of-Distribution Detection for Medical Imaging

A generative flow for conditional sampling via optimal transport

Synthesizing Forestry Images Conditioned on Plant Phenotype Using a Generative Adversarial Network

A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications

DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models

DiffFlow: A Unified SDE Framework for Score-Based Diffusion Models and Generative Adversarial Networks

Generative Adversarial Networks for Dental Patient Identity Protection in Orthodontic Educational Imaging

Disentanglement in a GAN for Unconditional Speech Synthesis

CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular Data Synthesis

Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals

TemperatureGAN: Generative Modeling of Regional Atmospheric Temperatures

Synthetic Demographic Data Generation for Card Fraud Detection Using GANs

A Simple and Effective Baseline for Attentional Generative Adversarial Networks