Adversarial Learning

Adversarial learning is a machine learning technique that improves model robustness and fairness by pitting two neural networks against each other—a generator and a discriminator—in a competitive training process. Current research focuses on applications across diverse fields, including improving fairness in predictive analytics, enhancing robustness in reinforcement learning and domain adaptation, and mitigating the effects of adversarial attacks on various models. This approach is significant because it addresses critical limitations of standard machine learning methods, leading to more reliable and equitable outcomes in various applications, from financial risk assessment to medical image analysis and autonomous systems.

Papers

July 24, 2023

FaFCNN: A General Disease Classification Framework Based on Feature Fusion Neural Networks
Menglin Kong, Shaojie Zhao, Juan Cheng, Xingquan Li, Ri Su, Muzhou Hou, Cong Cao
Adversarial Learning Feature Fusion Feature Alignment Disease Classification Feature Correlation

July 20, 2023

July 19, 2023

July 18, 2023

Adversarial Bayesian Augmentation for Single-Source Domain Generalization
Sheng Cheng, Tejas Gokhale, Yezhou Yang
Adversarial Learning Unseen Domain Image Augmentation Adversarial Augmentation Single Source Domain Generalization

July 14, 2023

July 13, 2023

Why Guided Dialog Policy Learning performs well? Understanding the role of adversarial learning and its alternative
Sho Shimoyama, Tetsuro Morimura, Kenshi Abe, Toda Takamichi, Yuta Tomomatsu, Masakazu Sugiyama, Asahi Hentona, Yuuki Azuma, Hirotaka Ninomiya
Reinforcement Learning Integral Role Adversarial Learning Dialogue Policy Dialog Policy Learning

July 11, 2023

Line Art Colorization of Fakemon using Generative Adversarial Neural Networks
Erick Oliveira Rodrigues, Esteban Clua, Giovani Bernardes Vitor
Adversarial Learning Generative Adversarial Generative Approach Line Art Colorization

July 7, 2023

RADAR: Robust AI-Text Detection via Adversarial Learning
Xiaomeng Hu, Pin-Yu Chen, Tsung-Yi Ho
Adversarial Learning Text Detection Wave Over the Horizon Radar Human Written Text AI Text Detection AI Text Detector

July 4, 2023

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios
Baorui Dai, Gaëtan Frusque, Tianfu Li, Qi Li, Olga Fink
Unsupervised Domain Adaptation Adversarial Learning Industrial Disturbing Noise Domain Adversarial Domain Specific Task Fault Diagnosis Method Wavelet Packet Transform

July 3, 2023

Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives
Danele Lunghi, Alkis Simitsis, Olivier Caelen, Gianluca Bontempi
Adversarial Attack Technical Challenge Adversarial Learning Synthesized View Fraud Detection

July 1, 2023

CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular Data Synthesis
Abdallah Alshantti, Damiano Varagnolo, Adil Rasheed, Aria Rahmati, Frank Westad
Generative Adversarial Network Synthetic Data Adversarial Learning Synthetic Tabular Data Tabular Data Synthesis GAN Baseline

June 26, 2023

June 22, 2023

MFCCGAN: A Novel MFCC-Based Speech Synthesizer Using Adversarial Learning
Mohammad Reza Hasanabadi Majid Behdad Davood Gharavian
Adversarial Learning Long Form Novel Speech Intelligibility State of the Art GAN Modern Vocoders Speech Synthesizer

June 19, 2023

Adversarial Learning

Papers

FaFCNN: A General Disease Classification Framework Based on Feature Fusion Neural Networks

A LLM Assisted Exploitation of AI-Guardian

Deceptive Alignment Monitoring

Forecasting Early with Meta Learning

Towards Building More Robust Models with Frequency Bias

Adversarial Bayesian Augmentation for Single-Source Domain Generalization

Generative adversarial networks for data-scarce spectral applications

Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification

Why Guided Dialog Policy Learning performs well? Understanding the role of adversarial learning and its alternative

Line Art Colorization of Fakemon using Generative Adversarial Neural Networks

RADAR: Robust AI-Text Detection via Adversarial Learning

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios

Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives

CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular Data Synthesis

Transfer: Cross Modality Knowledge Transfer using Adversarial Networks -- A Study on Gesture Recognition

The race to robustness: exploiting fragile models for urban camouflage and the imperative for machine learning security

Pseudo-Trilateral Adversarial Training for Domain Adaptive Traversability Prediction

MFCCGAN: A Novel MFCC-Based Speech Synthesizer Using Adversarial Learning

Learning Models of Adversarial Agent Behavior under Partial Observability

Probabilistic Matching of Real and Generated Data Statistics in Generative Adversarial Networks