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

January 18, 2022

GANmouflage: 3D Object Nondetection with Texture Fields
Rui Guo, Jasmine Collins, Oscar de Lima, Andrew Owens
3D Object Detection Adversarial Learning 3D Object Complex Object Shape Texture Field

January 17, 2022

H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression
Niccoló Marini, Manfredo Atzori, Sebastian Otálora, Stephane Marchand-Maillet, Henning Müller
Convolutional Neural Network Adversarial Learning Computational Pathology Hematoxylin and Eosin Eosin Stained Stain Invariant Stain Variation

January 12, 2022

Optimizing Prediction of MGMT Promoter Methylation from MRI Scans using Adversarial Learning
Sauman Das
Adversarial Learning Magnetic Resonance Imaging Glioblastoma Multiforme

January 11, 2022

Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task
Himashi Peiris, Zhaolin Chen, Gary Egan, Mehrtash Harandi
Adversarial Learning Segmentation Task Solution Path Brain Tumor Segmentation Brain Tumor Robust Segmentation BRAT Challenge Virtual Adversarial Training Reciprocal Learning

January 7, 2022

United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI
Jianfeng Zhao, Dengwang Li, Shuo Li
Data Detection Adversarial Learning Multi Modal Feature Liver Tumor Segmentation

December 28, 2021

December 26, 2021

December 23, 2021

NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning
Tony Ng, Hyo Jin Kim, Vincent Lee, Daniel DeTone, Tsun-Yi Yang, Tianwei Shen, Eddy Ilg, Vassileios Balntas, Krystian Mikolajczyk, Chris Sweeney
Adversarial Learning Image Reconstruction Reconstruction Network Feature Encoder

December 21, 2021

December 20, 2021

Adversarially Robust Stability Certificates can be Sample-Efficient
Thomas T. C. K. Zhang, Stephen Tu, Nicholas M. Boffi, Jean-Jacques E. Slotine, Nikolai Matni
Dynamical System Adversarial Learning

December 19, 2021

Investigation of Densely Connected Convolutional Networks with Domain Adversarial Learning for Noise Robust Speech Recognition
Chia Yu Li, Ngoc Thang Vu
Neural Network Convolutional Neural Network Speech Recognition Adversarial Learning Comprehensive Investigation Domain Adversarial SE SPP DenseNet

December 18, 2021

Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks
Simone Marullo, Matteo Tiezzi, Marco Gori, Stefano Melacci
Deep Learning Strong Generalization Raw Data Adversarial Learning Adversary Agent Neural Generation Neural Classifier

December 14, 2021

December 7, 2021

Physics guided deep learning generative models for crystal materials discovery
Yong Zhao, Edirisuriya MD Siriwardane, Jianjun Hu
Generative Model GAN Model Adversarial Learning Theoretical Physic 3D Structure

December 6, 2021

Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review
Huda Ali Alatwi, Charles Morisset
Deep Learning Model Adversarial Example Adversarial Learning Systematic Review Network Intrusion General Adversarial

Adversarial Learning

Papers

GANmouflage: 3D Object Nondetection with Texture Fields

H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression

Optimizing Prediction of MGMT Promoter Methylation from MRI Scans using Adversarial Learning

Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task

United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI

DeepAdversaries: Examining the Robustness of Deep Learning Models for Galaxy Morphology Classification

Adversarial Learning for Incentive Optimization in Mobile Payment Marketing

Associative Adversarial Learning Based on Selective Attack

FRuDA: Framework for Distributed Adversarial Domain Adaptation

Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation

NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning

Adversarial Neural Networks for Error Correcting Codes

Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfaces

Adversarially Robust Stability Certificates can be Sample-Efficient

Investigation of Densely Connected Convolutional Networks with Domain Adversarial Learning for Noise Robust Speech Recognition

Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks

On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training

Compensating trajectory bias for unsupervised patient stratification using adversarial recurrent neural networks

Physics guided deep learning generative models for crystal materials discovery

Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review