Robust Deep

Robust deep learning focuses on developing deep neural networks (DNNs) that maintain high accuracy even when faced with noisy data, adversarial attacks, or variations in input distributions. Current research emphasizes improving robustness through techniques like adversarial training, ensemble methods, and biologically-inspired architectures (e.g., incorporating features of the visual cortex), as well as exploring novel activation functions and optimization strategies. This field is crucial for deploying DNNs in safety-critical applications (e.g., autonomous driving, medical diagnosis) and for enhancing the reliability and trustworthiness of AI systems more broadly.

Papers

November 27, 2023

Adaptive Image Registration: A Hybrid Approach Integrating Deep Learning and Optimization Functions for Enhanced Precision
Gabriel De Araujo, Shanlin Sun, Xiaohui Xie
Image Registration Image Pair Multidimensional Local Precision Rate Hybrid Deep Learning Robust Deep Optimization Method Deformation Field

November 1, 2023

A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma based on CT Images
Ni Yao, Hang Hu, Kaicong Chen, Chen Zhao, Yuan Guo, Boya Li, Jiaofen Nan, Yanting Li, Chuang Han, Fubao Zhu, Weihua Zhou, Li Tian
Deep Learning Uncertainty Estimation CT Image Robust Deep Renal Cell Carcinoma

October 17, 2023

A Robust Deep Learning System for Motor Bearing Fault Detection: Leveraging Multiple Learning Strategies and a Novel Double Loss Function
Khoa Tran, Lam Pham, Vy-Rin Nguyen, Ho-Si-Hung Nguyen
Deep Learning Loss Function Fault Detection Robust Deep Motor Health Monitoring

September 7, 2023

Adversarially Robust Deep Learning with Optimal-Transport-Regularized Divergences
Jeremiah Birrell, Mohammadreza Ebrahimi
Adversarial Attack Adversarial Training Adversarial Robustness Optimal Transport Adversarial Sample Robust Deep

August 16, 2023

HyperSNN: A new efficient and robust deep learning model for resource constrained control applications
Zhanglu Yan, Shida Wang, Kaiwen Tang, Weng-Fai Wong
Spiking Neural Network Edge Computing Hyperdimensional Computing New Resource Robust Deep Control System AI Benchmark

August 2, 2023

Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks
Jun Guo, Aishan Liu, Xingyu Zheng, Siyuan Liang, Yisong Xiao, Yichao Wu, Xianglong Liu
Robust Deep Inference Time Model Stealing Attack Social Isolation Automatic Induction Defense Model

July 26, 2023

Deep Robust Multi-Robot Re-localisation in Natural Environments
Milad Ramezani, Ethan Griffiths, Maryam Haghighat, Alex Pitt, Peyman Moghadam
Multi Robot LiDAR Data Robust Deep LiDAR Based Localization Global Re Localization Relative Localization Wilderness Search Cross Modality Image

July 14, 2023

June 8, 2023

A Gradient-based Approach for Online Robust Deep Neural Network Training with Noisy Labels
Yifan Yang, Alec Koppel, Zheng Zhang
Noisy Label Gradient Based Gradient Method Robust Deep Scalable Training

June 6, 2023

Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings
Klim Kireev, Maksym Andriushchenko, Carmela Troncoso, Nicolas Flammarion
Native Robustness Adversarial Training Adversarial Robustness Universal Image Robust Deep Categorical Data

May 12, 2023

Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation
Jialiang Sun, Wen Yao, Tingsong Jiang, Xiaoqian Chen
Multi Fidelity Robust Deep Semantic Adversarial Efficient Search Robust Neural Architecture Composite Attack Robust Architecture

April 19, 2023

Wavelets Beat Monkeys at Adversarial Robustness
Jingtong Su, Julia Kempe
Adversarial Robustness Adversarial Noise Robust Deep Wavelet Based

April 8, 2023

Robust Deep Learning Models Against Semantic-Preserving Adversarial Attack
Dashan Gao, Yunce Zhao, Yinghua Yao, Zeqi Zhang, Bifei Mao, Xin Yao
Adversarial Attack Adversarial Training Adversarial Perturbation Adversarial Text Robust Training Robust Deep

March 14, 2023

Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology
Rao Muhammad Umer, Armin Gruber, Sayedali Shetab Boushehri, Christian Metak, Carsten Marr
Convolutional Neural Network Robust Deep Cell Classification Blood Cell Classification Imbalanced Domain Single Cell Image

February 19, 2023

Physics-aware deep learning framework for linear elasticity
Arunabha M. Roy, Rikhi Bose
Physic Informed Neural Network Deep Learning Framework Physic Informed Robust Deep Robust Neural Network Nonlinear Elasticity

February 5, 2023

Rethinking Robust Contrastive Learning from the Adversarial Perspective
Fatemeh Ghofrani, Mehdi Yaghouti, Pooyan Jamshidi
Contrastive Learning Adversarial Training Robust Deep Adversarial Perspective Robust Contrastive Learning

December 6, 2022

Training Neural Networks on Data Sources with Unknown Reliability
Alexander Capstick, Francesca Palermo, Tianyu Cui, Payam Barnaghi
Deep Neural Network Raw Data Data Quality Robust Deep Model Weight Unreliable Source Plasticity Loss

November 18, 2022

A Tale of Two Cities: Data and Configuration Variances in Robust Deep Learning
Guanqin Zhang, Jiankun Sun, Feng Xu, H. M. N. Dilum Bandara, Shiping Chen, Yulei Sui, Tim Menzies
Deep Learning Deep Neural Network Native Robustness Raw Data Urban Environment DNN Model Robust Deep Cautionary TALE DNN Robustness Parameter Configuration

November 14, 2022

Robust Deep Learning for Autonomous Driving
Charles Corbière
Autonomous Driving Robust Deep Model Confidence Reliable Uncertainty Uncertainty Measure Class Probability Evidential Model

Robust Deep

Papers

Adaptive Image Registration: A Hybrid Approach Integrating Deep Learning and Optimization Functions for Enhanced Precision

A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma based on CT Images

A Robust Deep Learning System for Motor Bearing Fault Detection: Leveraging Multiple Learning Strategies and a Novel Double Loss Function

Adversarially Robust Deep Learning with Optimal-Transport-Regularized Divergences

HyperSNN: A new efficient and robust deep learning model for resource constrained control applications

Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks

Deep Robust Multi-Robot Re-localisation in Natural Environments

Omnipotent Adversarial Training in the Wild

Multiplicative update rules for accelerating deep learning training and increasing robustness

A Gradient-based Approach for Online Robust Deep Neural Network Training with Noisy Labels

Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings

Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation

Wavelets Beat Monkeys at Adversarial Robustness

Robust Deep Learning Models Against Semantic-Preserving Adversarial Attack

Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology

Physics-aware deep learning framework for linear elasticity

Rethinking Robust Contrastive Learning from the Adversarial Perspective

Training Neural Networks on Data Sources with Unknown Reliability

A Tale of Two Cities: Data and Configuration Variances in Robust Deep Learning

Robust Deep Learning for Autonomous Driving