Generative Adversarial

Generative Adversarial Networks (GANs) are a class of machine learning models designed to generate new data instances that resemble a training dataset. Current research focuses on improving GAN performance and stability across diverse applications, including image enhancement, speech synthesis, and data augmentation, often employing architectures like HiFi-GAN and variations of GANs combined with other neural network types (e.g., autoencoders, transformers). This work is significant due to GANs' ability to address data scarcity issues, improve the quality of synthetic data for various tasks, and enhance the robustness of AI systems against adversarial attacks.

Papers

March 11, 2022

GSDA: Generative Adversarial Network-based Semi-Supervised Data Augmentation for Ultrasound Image Classification
Zhaoshan Liu, Qiujie Lv, Chau Hung Lee, Lei Shen
Generative Adversarial Network Data Augmentation Generative Adversarial Ultrasound Image Ultrasound Image Analysis

March 9, 2022

Universal Regression with Adversarial Responses
Moïse Blanchard, Patrick Jaillet
Generative Adversarial Moving Horizon Adversarial Response

March 3, 2022

Fail-Safe Adversarial Generative Imitation Learning
Philipp Geiger, Christoph-Nikolas Straehle
Feature Imitation Generative Adversarial Reachability Analysis Safe Policy Generative Adversarial Imitation Learning

February 21, 2022

GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty
Wei Wayne Chen, Doksoo Lee, Oluwaseyi Balogun, Wei Chen
Product Design Robust Optimization Generative Adversarial Geometric Optimization Ct Gan Hierarchical Deep Generative Geometric Uncertainty

February 16, 2022

Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing
Zhiyan Chen, Burak Kantarci
Generative Adversarial Network Generative Adversarial Adversarial Detection Detection Network State of the Art GAN Mobile Crowdsensing GAN Discriminator

February 15, 2022

Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving
Chuqing Hu, Sinclair Hudson, Martin Ethier, Mohammad Al-Sharman, Derek Rayside, William Melek
Autonomous Driving Domain Adaptation Classification Code Generative Adversarial Lane Detection Sim to Real Domain Adaptation

February 7, 2022

Learning fair representation with a parametric integral probability metric
Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, Yongdai Kim
Generative Adversarial Fair Representation AI Fairness ApeRIodic SEmi Parametric

January 28, 2022

Existence and Estimation of Critical Batch Size for Training Generative Adversarial Networks with Two Time-Scale Update Rule
Naoki Sato, Hideaki Iiduka
Generative Adversarial Network Estimation Task Learning Rate Generative Adversarial Time Scale Batch Size

January 8, 2022

Counteracting Dark Web Text-Based CAPTCHA with Generative Adversarial Learning for Proactive Cyber Threat Intelligence
Ning Zhang, Mohammadreza Ebrahimi, Weifeng Li, Hsinchun Chen
Generative Adversarial Dark Web CAPtcha Solver Threat Hunting Darknet Traffic

December 15, 2021

Deep Generative Models for Geometric Design Under Uncertainty
Wei Wayne Chen, Doksoo Lee, Wei Chen
High Uncertainty Anticipation Generative Adversarial Deep Generative Model Geometric Optimization Geometric Shape Overlay Network

December 3, 2021

On the Existence of the Adversarial Bayes Classifier (Extended Version)
Pranjal Awasthi, Natalie S. Frank, Mehryar Mohri
Adversarial Robustness Generative Adversarial Extended Version Bayes Rule Bayes Optimal Certifiable Robustness

December 1, 2021

FaceTuneGAN: Face Autoencoder for Convolutional Expression Transfer Using Neural Generative Adversarial Networks
Nicolas Olivier, Kelian Baert, Fabien Danieau, Franck Multon, Quentin Avril
Supervised Autoencoder Generative Adversarial Facial Geometry Facial Identity 3D Facial GAN Generated Face Facial Expression Transfer Expression Transfer

November 24, 2021

Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes
Sam Bond-Taylor, Peter Hessey, Hiroshi Sasaki, Toby P. Breckon, Chris G. Willcocks
Image Generation Generative Adversarial Vector Quantization Discrete Diffusion Vector Quantized Thought Empowers Transformer Image Likelihood

November 10, 2021

Training Generative Adversarial Networks with Adaptive Composite Gradient
Huiqing Qi, Fang Li, Shengli Tan, Xiangyun Zhang
Generative Adversarial Network GAN Model Gradient Based Generative Adversarial Gradient Free

Generative Adversarial

Papers

GSDA: Generative Adversarial Network-based Semi-Supervised Data Augmentation for Ultrasound Image Classification

Universal Regression with Adversarial Responses

Fail-Safe Adversarial Generative Imitation Learning

GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty

Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing

Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving

Learning fair representation with a parametric integral probability metric

Existence and Estimation of Critical Batch Size for Training Generative Adversarial Networks with Two Time-Scale Update Rule

Counteracting Dark Web Text-Based CAPTCHA with Generative Adversarial Learning for Proactive Cyber Threat Intelligence

Deep Generative Models for Geometric Design Under Uncertainty

On the Existence of the Adversarial Bayes Classifier (Extended Version)

FaceTuneGAN: Face Autoencoder for Convolutional Expression Transfer Using Neural Generative Adversarial Networks

Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes

Training Generative Adversarial Networks with Adaptive Composite Gradient